Awesome Physiscs-Informed Machine Learning (Neuural Networks)

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Physics-Informed Machine Learning (PIML) and Physics-Informed Neural Networks (PINNs). This repository is designed to be a comprehensive, well-organized knowledge base for researchers and developers working in the growing field of integrating physics with machine learning.

To ensure that the community stays up to date with the latest breakthroughs, our repository is automatically updated with new PINN/PIML-related research papers from arXiv. This feature guarantees access to cutting-edge developments, making it an invaluable resource for anyone exploring physics-constrained learning methods.

[!NOTE] 📢 Announcement: Our paper from AIT Lab is now available on SSRN!
Title: Not Just Another Survey on Physics-Informed Neural Networks (PINNs): Foundations, Advances, and Open Problems
If you find this paper interesting, please consider citing our work. Thank you for your support!

@article{somvanshi2025not,
  title={Not Just Another Survey on Physics-Informed Neural Networks (PINNs): Foundations, Advances, and Open Problems},
  author={Somvanshi, Shriyank and Aibinu, Mathew Olajiire and Chakraborty, Rohit and Islam, Md Monzurul and Mimi, Mahmuda Sultana and Koirala, Dipti and Brotee, Shamyo and Dutta, Anandi and Das, Subasish},
  journal={Available at SSRN},
  year={2025}
}

Whether you're a researcher modeling complex physical systems, a developer building physics-guided models, or an enthusiast in scientific machine learning, this collection serves as a centralized hub for everything related to PIML, PINNs, and the broader integration of domain knowledge into learning systems, enriched by original peer-reviewed contributions to the field.

Last Updated

October 12, 2026 at 04:17:11 AM UTC

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Papers (1138)

• OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics
• Hamiltonian Learning via Inverse Physics-Informed Neural Networks
• R-PINN: Recovery-type a-posteriori estimator enhanced adaptive PINN
• Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
• Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics
• TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses
• Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks
• LT-PINN: Lagrangian Topology-conscious Physics-informed Neural Network for Boundary-focused Engineering Optimization
• BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
• Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems
• Neural Tangent Kernel Analysis to Probe Convergence in Physics-informed Neural Solvers: PIKANs vs. PINNs
• Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties
• Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
• Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods
• Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
• Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs
• Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
• Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
• SF^2^2Bench: Evaluating Data-Driven Models for Compound Flood Forecasting in South Florida
• Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
• A Bayesian PINN Framework for Barrow-Tsallis Holographic Dark Energy with Neutrinos: Toward a Resolution of the Hubble Tension
• An Approximation Theory Perspective on Machine Learning
• Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
• MoPINNEnKF: Iterative Model Inference using generic-PINN-based ensemble Kalman filter
• DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
• Cluster Reconstruction in Electromagnetic Calorimeters Using Machine Learning Methods
• Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
• Machine learning meets \mathfrak{su}(n)\mathfrak{su}(n) Lie algebra: Enhancing quantum dynamics learning with exact trace conservation
• On the definition and importance of interpretability in scientific machine learning
• CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs
• PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
• Locking-Free Training of Physics-Informed Neural Network for Solving Nearly Incompressible Elasticity Equations
• A Physics-Informed Learning Framework to Solve the Infinite-Horizon Optimal Control Problem
• Are Statistical Methods Obsolete in the Era of Deep Learning?
• Godunov Loss Functions for Modelling of Hyperbolic Conservation Laws
• Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
• A data augmentation strategy for deep neural networks with application to epidemic modelling
• Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
• Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks
• Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference
• Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
• Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
• KITINet: Kinetics Theory Inspired Network Architectures with PDE Simulation Approaches
• SetPINNs: Set-based Physics-informed Neural Networks
• Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks
• A Unified Framework for Simultaneous Parameter and Function Discovery in Differential Equations
• Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces
• Machine learning on manifolds for inverse scattering: Lipschitz stability analysis
• Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows
• Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks
• Modelling Mosquito Population Dynamics using PINN-derived Empirical Parameters
• Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems
• Safe Physics-Informed Machine Learning for Dynamics and Control
• PINNs Algorithmic Framework for Simulation of Nonlinear Burgers' Type Models
• Enhancing Physics-Informed Neural Networks Through Feature Engineering
• Which Optimizer Works Best for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks?
• Learning Mappings in Mesh-based Simulations
• Physics-Informed Priors with Application to Boundary Layer Velocity
• A Hybrid Neural Network -- Polynomial Series Scheme for Learning Invariant Manifolds of Discrete Dynamical Systems
• A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction
• Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets: the square lattice
• Stability Analysis of Physics-Informed Neural Networks via Variational Coercivity, Perturbation Bounds, and Concentration Estimates
• NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification
• Quantum Recurrent Embedding Neural Network
• Interpretability and Generalization Bounds for Learning Spatial Physics
• Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
• Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
• SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation
• GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing
• Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
• Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures
• Supercharging Graph Transformers with Advective Diffusion
• Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification
• Rank Inspired Neural Network for solving linear partial differential equations
• Mask-PINNs: Regulating Feature Distributions in Physics-Informed Neural Networks
• Solving a class of stochastic optimal control problems by physics-informed neural networks
• High precision PINNs in unbounded domains: application to singularity formulation in PDEs
• Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures
• Méthode de quadrature pour les PINNs fondée théoriquement sur la hessienne des résiduels
• A Neural-Operator Surrogate for Platelet Deformation Across Capillary Numbers
• Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
• Convolution-weighting method for the physics-informed neural network: A Primal-Dual Optimization Perspective
• Least Squares with Equality constraints Extreme Learning Machines for the resolution of PDEs
• Physics-informed network paradigm with data generation and background noise removal for diverse distributed acoustic sensing applications
• Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
• BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
• Fully Differentiable Lagrangian Convolutional Neural Network for Physics-Informed Precipitation Nowcasting
• Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization
• B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling
• A generative modeling / Physics-Informed Neural Network approach to random differential equations
• Unraveling particle dark matter with Physics-Informed Neural Networks
• Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
• OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems
• Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks
• Robust Power System State Estimation using Physics-Informed Neural Networks
• Physics-Guided Dual Implicit Neural Representations for Source Separation
• Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models
• Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane
• Machine Learning in Acoustics: A Review and Open-Source Repository
• Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
• Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
• Noisy PDE Training Requires Bigger PINNs
• Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study
• Differentiable Stellar Atmospheres with Physics-Informed Neural Networks
• Towards Robust Surrogate Models: Benchmarking Machine Learning Approaches to Expediting Phase Field Simulations of Brittle Fracture
• Understanding Malware Propagation Dynamics through Scientific Machine Learning
• Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures
• Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
• Quasi-Random Physics-informed Neural Networks
• PDE-aware Optimizer for Physics-informed Neural Networks
• Physics-informed neural networks for high-dimensional solutions and snaking bifurcations in nonlinear lattices
• Toward accurate RUL and SOH estimation using reinforced graph-based PINNs enhanced with dynamic weights
• Energy Dissipation Rate Guided Adaptive Sampling for Physics-Informed Neural Networks: Resolving Surface-Bulk Dynamics in Allen-Cahn Systems
• Universal Physics Simulation: A Foundational Diffusion Approach
• Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition
• MVPinn: Integrating Milne-Eddington Inversion with Physics-Informed Neural Networks for GST/NIRIS Observations
• WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs with Physics-Informed Neural Networks
• Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
• Physics-informed machine learning: A mathematical framework with applications to time series forecasting
• Physical Informed Neural Networks for modeling ocean pollutant
• Moderate Adaptive Linear Units (MoLU)
• Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review
• Learning from Imperfect Data: Robust Inference of Dynamic Systems using Simulation-based Generative Model
• Polaritonic Machine Learning for Graph-based Data Analysis
• Kernel-Adaptive PI-ELMs for Forward and Inverse Problems in PDEs with Sharp Gradients
• Compliance Minimization via Physics-Informed Gaussian Processes
• Physics-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
• Low-latency Forecasts of Kilonova Light Curves for Rubin and ZTF
• Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
• Adaptive feature capture method for solving partial differential equations with low regularity solutions
• A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading
• Improving physics-informed neural network extrapolation via transfer learning and adaptive activation functions
• Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems
• Machine Learning-aided Optimal Control of a noisy qubit
• AI-Accelerated Flow Simulation: A Robust Auto-Regressive Framework for Long-Term CFD Forecasting
• Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
• An explainable operator approximation framework under the guideline of Green's function
• Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations
• Impact of Ethanol and Methanol on NOx Emissions in Ammonia-Methane Combustion: ReaxFF Simulations and ML-Based Extrapolation
• Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization
• Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
• Quantum computational sensing using quantum signal processing, quantum neural networks, and Hamiltonian engineering
• GeoHNNs: Geometric Hamiltonian Neural Networks
• Adaptive feature capture method for solving partial differential equations with near singular solutions
• LArTPC hit-based topology classification with quantum machine learning and symmetry
• Inverse Design using Physics-Informed Quantum GANs for Tailored Absorption in Dielectric Metasurfaces
• Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics
• Learning Long-Range Representations with Equivariant Messages
• Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
• Quantum-Efficient Convolution through Sparse Matrix Encoding and Low-Depth Inner Product Circuits
• Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
• Linear Stability Analysis of Physics-Informed Random Projection Neural Networks for ODEs
• Improving Neural Network Training using Dynamic Learning Rate Schedule for PINNs and Image Classification
• PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems
• DEM-NeRF: A Neuro-Symbolic Method for Scientific Discovery through Physics-Informed Simulation
• Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy
• LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
• Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
• Separated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs
• Double descent: When do neural quantum states generalize?
• Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
• Physics-Informed Neural Network Approaches for Sparse Data Flow Reconstruction of Unsteady Flow Around Complex Geometries
• Deep Operator Networks for Bayesian Parameter Estimation in PDEs
• Predictive calibration for digital sun sensors using sparse submanifold convolutional neural networks
• A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks
• QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
• Quantum Spectral Reasoning: A Non-Neural Architecture for Interpretable Machine Learning
• Solved in Unit Domain: JacobiNet for Differentiable Coordinate Transformations
• Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization
• Overcoming the Loss Conditioning Bottleneck in Optimization-Based PDE Solvers: A Novel Well-Conditioned Loss Function
• Physics-Informed Neural Network for Elastic Wave-Mode Separation
• Improved Training Strategies for Physics-Informed Neural Networks using Real Experimental Data in Aluminum Spot Welding
• BubbleONet: A Physics-Informed Neural Operator for High-Frequency Bubble Dynamics
• Physical Scales Matter: The Role of Receptive Fields and Advection in Satellite-Based Thunderstorm Nowcasting with Convolutional Neural Networks
• Leveraging Deep Learning for Physical Model Bias of Global Air Quality Estimates
• Fast, Convex and Conditioned Network for Multi-Fidelity Vectors and Stiff Univariate Differential Equations
• Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
• Exploration of Hepatitis B Virus Infection Dynamics through Physics-Informed Deep Learning Approach
• Hybrid Approaches for Black Hole Spin Estimation: From Classical Spectroscopy to Physics-Informed Machine Learning
• Generalising Traffic Forecasting to Regions without Traffic Observations
• LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
• A Physics-informed Deep Operator for Real-Time Freeway Traffic State Estimation
• Prediction error certification for PINNs: Theory, computation, and application to Stokes flow
• Learning Satellite Attitude Dynamics with Physics-Informed Normalising Flow
• Chaos into Order: Neural Framework for Expected Value Estimation of Stochastic Partial Differential Equations
• Time Marching Neural Operator FE Coupling: AI Accelerated Physics Modeling
• Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
• Estimating carbon pools in the shelf sea environment: reanalysis or model-informed machine learning?
• Physics-informed deep operator network for traffic state estimation
• Regime-Aware Time Weighting for Physics-Informed Neural Networks
• Sub-Sequential Physics-Informed Learning with State Space Model
• Machine Learning-Based AES Key Recovery via Side-Channel Analysis on the ASCAD Dataset
• Kourkoutas-Beta: A Sunspike-Driven Adam Optimizer with Desert Flair
• Universal on-chip polarization handling with deep photonic networks
• Strategies for training point distributions in physics-informed neural networks
• Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques
• Comparison of derivative-free and gradient-based minimization for multi-objective compositional design of shape memory alloys
• Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
• PIANO: Physics Informed Autoregressive Network
• A Hybrid Discontinuous Galerkin Neural Network Method for Solving Hyperbolic Conservation Laws with Temporal Progressive Learning
• Physics-informed neural network for fatigue life prediction of irradiated austenitic and ferritic/martensitic steels
• HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems
• Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
• Automated discovery of finite volume schemes using Graph Neural Networks
• PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
• Multi-timescale time encoding for CNN prediction of Fenna-Matthews-Olson energy-transfer dynamics
• ChemKANs for Combustion Chemistry Modeling and Acceleration
• Efficient PINNs via Multi-Head Unimodular Regularization of the Solutions Space
• Constraining the Cosmological Constant from Stellar Orbits Around Sgr A* Using Physics-Informed Neural Networks
• Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management
• Fast Convergence Rates for Subsampled Natural Gradient Algorithms on Quadratic Model Problems
• Polynomial Chaos Expansion for Operator Learning
• Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction
• Molecular Machine Learning in Chemical Process Design
• Neural Spline Operators for Risk Quantification in Stochastic Systems
• Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
• Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
• An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network
• Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
• Adaptive Physics-Informed Neural Networks with Multi-Category Feature Engineering for Hydrogen Sorption Prediction in Clays, Shales, and Coals
• Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
• HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction
• Local Feature Filtering for Scalable and Well-Conditioned Domain-Decomposed Random Feature Methods
• Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks
• Expedited Noise Spectroscopy of Transmon Qubits
• Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks
• Quantum Reservoir Computing Implementations for Classical and Quantum Problems
• RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
• Towards Digital Twins for Optimal Radioembolization
• HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions
• Neuro-Spectral Architectures for Causal Physics-Informed Networks
• A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes
• SPINN: An Optimal Self-Supervised Physics-Informed Neural Network Framework
• Universality of physical neural networks with multivariate nonlinearity
• Improved Physics-informed neural networks loss function regularization with a variance-based term
• Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
• IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
• DEQuify your force field: More efficient simulations using deep equilibrium models
• A DEM-driven machine learning framework for abrasive wear prediction
• MasconCube: Fast and Accurate Gravity Modeling with an Explicit Representation
• Facet: highly efficient E(3)-equivariant networks for interatomic potentials
• ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
• Causal PDE-Control Models: A Structural Framework for Dynamic Portfolio Optimization
• Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
• WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
• SciML Agents: Write the Solver, Not the Solution
• Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
• Physics-informed neural network solves minimal surfaces in curved spacetime
• Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study
• Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with Physics-informed Residual Connected Neural Operator
• PBPK-iPINNs : Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models
• Comparative Analysis of Wave Scattering Numerical Modeling Using the Boundary Element Method and Physics-Informed Neural Networks
• Quantum Noise Tomography with Physics-Informed Neural Networks
• Extraction of Dihadron Fragmentation Functions at NNLO with and without Neural Networks
• Stabilizing PINNs: A regularization scheme for PINN training to avoid unstable fixed points of dynamical systems
• Reconstructing High-fidelity Plasma Turbulence with Data-driven Tuning of Diffusion in Low Resolution Grids
• Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs
• Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Realistic Synthetic Conditions
• A Physics-Informed Neural Networks-Based Model Predictive Control Framework for SIRSIR Epidemics
• A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
• Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
• Unified Spatiotemopral Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics
• Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
• Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics
• Evidential Physics-Informed Neural Networks for Scientific Discovery
• Data-driven discovery of governing equation for sheared granular materials in steady and transient states
• Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
• Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
• PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models
• Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
• Solving Partial Differential Equations with Random Feature Models
• PACMANN: Point Adaptive Collocation Method for Artificial Neural Networks
• Machine Learning for Quantum Noise Reduction
• Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
• Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints
• SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
• Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
• Model-Agnostic AI Framework with Explicit Time Integration for Long-Term Fluid Dynamics Prediction
• THINNs: Thermodynamically Informed Neural Networks
• Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation
• Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications
• Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders
• PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
• Learning Greens Operators through Hierarchical Neural Networks Inspired by the Fast Multipole Method
• Neural Networks as Surrogate Solvers for Time-Dependent Accretion Disk Dynamics
• BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase
• Reparameterizing 4DVAR with neural fields
• Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
• Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
• Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
• Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
• Towards generalizable deep ptychography neural networks
• Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks
• White-box machine learning for uncovering physically interpretable dimensionless governing equations for granular materials
• Weight-Space Linear Recurrent Neural Networks
• DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
• MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
• Randomized Matrix Sketching for Neural Network Training and Gradient Monitoring
• Nondestructive characterization of laser-cooled atoms using machine learning
• Fast training of accurate physics-informed neural networks without gradient descent
• Architecturally Constrained Solutions to Ill-Conditioned Problems in QUBIC
• Physics-Informed Machine Learning Approach in Augmenting RANS Models Using DNS Data and DeepInsight Method on FDA Nozzle
• Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
• Nyström-Accelerated Primal LS-SVMs: Breaking the O(an^3)O(an^3) Complexity Bottleneck for Scalable ODEs Learning
• Quantifying constraint hierarchies in Bayesian PINNs via per-constraint Hessian decomposition
• Physics-Informed Machine Learning in Biomedical Science and Engineering
• Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
• Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
• Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
• Towards Fast Option Pricing PDE Solvers Powered by PIELM
• Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework
• Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
• Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
• AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
• StruSR: Structure-Aware Symbolic Regression with Physics-Informed Taylor Guidance
• Learning Non-Ideal Vortex Flows Using the Differentiable Vortex Particle Method
• Diffusion-Guided Renormalization of Neural Systems via Tensor Networks
• PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling
• Mass Conservation on Rails - Rethinking Physics-Informed Learning of Ice Flow Vector Fields
• Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
• Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields
• A Morphology-Adaptive Random Feature Method for Inverse Source Problem of the Helmholtz Equation
• Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
• Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
• AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
• PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
• Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
• Temporal Lifting as Latent-Space Regularization for Continuous-Time Flow Models in AI Systems
• A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoring
• Neural PDE Solvers with Physics Constraints: A Comparative Study of PINNs, DRM, and WANs
• Leveraging Teleconnections with Physics-Informed Graph Attention Networks for Long-Range Extreme Rainfall Forecasting in Thailand
• Accelerating Natural Gradient Descent for PINNs with Randomized Nyström Preconditioning
• Near-Infrared Hyperspectral Imaging Applications in Food Analysis -- Improving Algorithms and Methodologies
• Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs
• Quantum machine learning and quantum-inspired methods applied to computational fluid dynamics: a short review
• Towards Symmetry-Aware Efficient Simulation of Quantum Systems and Beyond
• A Comprehensive Evaluation of Graph Neural Networks and Physics Informed Learning for Surrogate Modelling of Finite Element Analysis
• Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
• Physics-Informed Deep B-Spline Networks
• Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions
• AMStraMGRAM: Adaptive Multi-cutoff Strategy Modification for ANaGRAM
• A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
• Ensemble based Closed-Loop Optimal Control using Physics-Informed Neural Networks
• Prediction of Sea Ice Velocity and Concentration in the Arctic Ocean using Physics-informed Neural Network
• Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
• Decentralized Real-Time Planning for Multi-UAV Cooperative Manipulation via Imitation Learning
• Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
• A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
• Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization
• A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems
• A discrete physics-informed training for projection-based reduced order models with neural networks
• PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
• RGC: a radio AGN classifier based on deep learning. I. A semi-supervised model for the VLA images of bent radio AGNs
• Efficient Global-Local Fusion Sampling for Physics-Informed Neural Networks
• Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN) for Spaghetti Bridge Load Prediction
• Learning Robust Satellite Attitude Dynamics with Physics-Informed Normalising Flow
• Self-induced stochastic resonance: A physics-informed machine learning approach
• A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
• Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics
• Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
• Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
• Physics-Informed Latent Neural Operator for Real-time Predictions of time-dependent parametric PDEs
• Enforcing boundary conditions for physics-informed neural operators
• Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning
• Meshless solutions of PDE inverse problems on irregular geometries
• LieSolver: A PDE-constrained solver for IBVPs using Lie symmetries
• A Practitioner's Guide to Kolmogorov-Arnold Networks
• TrajectoryFlowNet: Lagrangian-Eulerian learning of flow field and trajectories
• A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
• Solving Infinite-Horizon Optimal Control Problems using the Extreme Theory of Functional Connections
• Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
• Defining Energy Indicators for Impact Identification on Aerospace Composites: A Physics-Informed Machine Learning Perspective
• HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs
• Fast PINN Eigensolvers via Biconvex Reformulation
• Structure-Preserving Physics-Informed Neural Network for the Korteweg--de Vries (KdV) Equation
• Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
• Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning
• Physics-Informed Neural Networks and Neural Operators for Parametric PDEs: A Human-AI Collaborative Analysis
• Machine-Learning Estimation of Energy Fractions in MHD Turbulence Modes
• Computed Tomography (CT)-derived Cardiovascular Flow Estimation Using Physics-Informed Neural Networks Improves with Sinogram-based Training: A Simulation Study
• Self-adaptive weighting and sampling for physics-informed neural networks
• Fuzzy Neural Network Performance and Interpretability of Quantum Wavefunction Probability Predictions
• Physics-Informed Neural Operators for Cardiac Electrophysiology
• Fill the gaps: continuous in time interpolation of fluid dynamical simulations
• Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework
• Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics
• Statistical learning on randomized data to verify quantum state approximate k-designs
• From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning
• Automated machine learning for physics-informed convolutional neural networks
• Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
• Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
• Seismic inversion using hybrid quantum neural networks
• NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
• Physics-Informed Design of Input Convex Neural Networks for Consistency Optimal Transport Flow Matching
• Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation
• SSTODE: Ocean-Atmosphere Physics-Informed Neural ODEs for Sea Surface Temperature Prediction
• MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
• E-PINNs: Epistemic Physics-Informed Neural Networks
• Towards a Machine Learning Solution for Hubble Tension: Physics-Informed Neural Network (PINN) Analysis of Tsallis Holographic Dark Energy in Presence of Neutrinos
• Physics-Informed Neural Networks for Gate Design using Quantum Optimal Control
• Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks
• Quantum physics informed neural networks for multi-variable partial differential equations
• Integration Matters for Learning PDEs with Backwards SDEs
• Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery
• One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
• PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI
• Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction
• Real-time distortion prediction in metallic additive manufacturing via a physics-informed neural operator approach
• Physics-Constrained Adaptive Neural Networks Enable Real-Time Semiconductor Manufacturing Optimization with Minimal Training Data
• Machine Learning Framework for Efficient Prediction of Quantum Wasserstein Distance
• Real-Time Physics-Aware Battery Health Monitoring from Partial Charging Profiles via Physics-Informed Neural Networks
• Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
• Enforcing hidden physics in physics-informed neural networks
• Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mapping
• Extended Physics Informed Neural Network for Hyperbolic Two-Phase Flow in Porous Media
• A Physics Informed Machine Learning Framework for Optimal Sensor Placement and Parameter Estimation
• Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
• Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation
• Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
• ZENN: A Thermodynamics-Inspired Computational Framework for Heterogeneous Data-Driven Modeling
• The Ensemble Kalman Inversion Race
• Performance Guarantees for Quantum Neural Estimation of Entropies
• RRaPINNs: Residual Risk-Aware Physics Informed Neural Networks
• Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models
• PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
• A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences
• Solving Heterogeneous Agent Models with Physics-informed Neural Networks
• A Physics-Informed U-net-LSTM Network for Data-Driven Seismic Response Modeling of Structures
• Physics-Informed Neural Networks for Thermophysical Property Retrieval
• Hard-Constrained Neural Networks with Physics-Embedded Architecture for Residual Dynamics Learning and Invariant Enforcement in Cyber-Physical Systems
• FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks
• Ga_2_2O_3_3 TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network
• GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels
• AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems
• Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning
• Physics-Informed Spiking Neural Networks via Conservative Flux Quantization
• Learning to Reconstruct: A Differentiable Approach to Muon Tracking at the LHC
• Forecasting India's Demographic Transition Under Fertility Policy Scenarios Using hybrid LSTM-PINN Model
• Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
• Time-series forecasting with multiphoton quantum states and integrated photonics
• Modeling and Inverse Identification of Interfacial Heat Conduction in Finite Layer and Semi-Infinite Substrate Systems via a Physics-Guided Neural Framework
• Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
• Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
• Fault-Tolerant Control of Steam Temperature in HRSG Superheater under Actuator Fault Using a Sliding Mode Observer and PINN
• Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
• Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
• ASPEN: An Adaptive Spectral Physics-Enabled Network for Ginzburg-Landau Dynamics
• Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins
• DAE-HardNet: A Physics Constrained Neural Network Enforcing Differential-Algebraic Hard Constraints
• xLSTM-PINN: Memory-Gated Spectral Remodeling for Physics-Informed Learning
• A Control Perspective on Training PINNs
• Hardware-inspired Continuous Variables Quantum Optical Neural Networks
• Physics-Informed Neural Networks for Source Inversion and Parameters Estimation in Atmospheric Dispersion
• Boosting probes of CP violation in the top Yukawa coupling with Deep Learning
• PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Spatiotemporal Prediction
• Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
• A new initialisation to Control Gradients in Sinusoidal Neural network
• Data-Driven Model for Elastomers under Simultaneous Thermal and Radiation Exposure
• PyMieDiff: A differentiable Mie scattering library
• Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
• On Parameter Identification in Three-Dimensional Elasticity and Discretisation with Physics-Informed Neural Networks
• Point Neuron Learning: A New Physics-Informed Neural Network Architecture
• Tensor-Compressed and Fully-Quantized Training of Neural PDE Solvers
• A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Field
• The Adaptive Vekua Cascade: A Differentiable Spectral-Analytic Solver for Physics-Informed Representation
• iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter
• On the failure of ReLU activation for physics-informed machine learning
• The Vekua Layer: Exact Physical Priors for Implicit Neural Representations via Generalized Analytic Functions
• Refining Graphical Neural Network Predictions Using Flow Matching for Optimal Power Flow with Constraint-Satisfaction Guarantee
• KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers
• Rethinking Physics-Informed Regression Beyond Training Loops and Bespoke Architectures
• Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning
• Autoregressive Neural Network Extrapolation of Quantum Spin Dynamics Across Time and Space
• A Physics-Embedded Dual-Learning Imaging Framework for Electrical Impedance Tomography
• Neural equilibria for long-term prediction of nonlinear conservation laws
• Multi-Trajectory Physics-Informed Neural Networks for HJB Equations with Hard-Zero Terminal Inventory: Optimal Execution on Synthetic & SPY Data
• AGN X-ray Reflection Spectroscopy with ML MYTORUS:Neural Posterior Estimation with Training on Observation-Driven Parameter Grids
• Physics-informed neural networks to solve inverse problems in unbounded domains
• Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries
• A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materials
• CARONTE: a Physics-Informed Extreme Learning Machine-Based Algorithm for Plasma Boundary Reconstruction in Magnetically Confined Fusion Devices
• Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
• TENG++: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets under General Boundary Conditions
• More Consistent Accuracy PINN via Alternating Easy-Hard Training
• BumpNet: A Sparse Neural Network Framework for Learning PDE Solutions
• Digital Twin-Driven Zero-Shot Fault Diagnosis of Axial Piston Pumps Using Fluid-Borne Noise Signals
• Self-Consistent Probability Flow for High-Dimensional Fokker-Planck Equations
• Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
• GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface
• MAD-NG: Meta-Auto-Decoder Neural Galerkin Method for Solving Parametric Partial Differential Equations
• Physics-Informed Neural Solvers for Periodic Quantum Eigenproblems
• Neural Measures for learning distributions of Random PDEs
• Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck Equations
• Spectral Analysis of Hard-Constraint PINNs: The Spatial Modulation Mechanism of Boundary Functions
• Differentiable Inverse Modeling with Physics-Constrained Latent Diffusion for Heterogeneous Subsurface Parameter Fields
• PHANTOM: Physics-Aware Adversarial Attacks against Federated Learning-Coordinated EV Charging Management System
• DBAW-PIKAN: Dynamic Balance Adaptive Weight Kolmogorov-Arnold Neural Network for Solving Partial Differential Equations
• Müntz-Szász Networks: Neural Architectures with Learnable Power-Law Bases
• Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part I: Basic Concepts, Neural Networks, and Variants
• Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification
• Optical Spiking Neural Networks via Rogue-Wave Statistics
• Soliton profiles: Classical Numerical Schemes vs. Neural Network - Based Solvers
• Deep Learning in Geotechnical Engineering: A Critical Assessment of PINNs and Operator Learning
• Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma
• Physics-informed Graph Neural Networks for Operational Flood Modeling
• Learning Density Functionals to Bridge Particle and Continuum Scales
• Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data
• Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect
• Solving nonlinear subsonic compressible flow in infinite domain via multi-stage neural networks
• Additive general integral equations in thermoelastic micromechanics of composites
• New RVE concept in thermoelasticity of periodic composites subjected to compact support loading
• A-PINN: Auxiliary Physics-informed Neural Networks for Structural Vibration Analysis in Continuous Euler-Bernoulli Beam
• Intrinsic-Metric Physics-Informed Neural Networks (IM-PINN) for Reaction-Diffusion Dynamics on Complex Riemannian Manifolds
• U-PINet: Physics-Informed Hierarchical Learning for Accurate and Fast 3D RCS Prediction
• Scaling Laws of Machine Learning for Optimal Power Flow
• Machine learning for radiative hydrodynamics in astrophysics
• Robust Physics Discovery from Highly Corrupted Data: A PINN Framework Applied to the Nonlinear Schrödinger Equation
• Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
• Physics-Based Decline Curve Analysis and Machine Learning for Temperature Forecasting in Enhanced Geothermal Systems: Utah FORGE
• A Non Linear Spectral Graph Neural Network Simulator for More Stable and Accurate Rollouts
• Dual-Level Models for Physics-Informed Multi-Step Time Series Forecasting
• Unsteady flow predictions around an obstacle using Geometry-Parameterized Dual-Encoder Physics-Informed Neural Network
• Building Envelope Inversion by Data-driven Interpretation of Ground Penetrating Radar
• Soft Partition-based KAPI-ELM for Multi-Scale PDEs
• Multi-Preconditioned LBFGS for Training Finite-Basis PINNs
• Integration Matters for Learning PDEs with Backward SDEs
• SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
• AC-PKAN: Attention-Enhanced and Chebyshev Polynomial-Based Physics-Informed Kolmogorov-Arnold Networks
• U-PINet: Physics-Informed Hierarchical Learning for Radar Cross Section Prediction via 3D Electromagnetic Scattering Reconstruction
• A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
• A universal linearized subspace refinement framework for neural networks
• Machine Learning Decoder for 5G NR PUCCH Format 0
• Generalized Reproducing Kernel Banach Spaces: A Functional Analytic Framework for Abstract Neural Networks
• Architecture-Optimization Co-Design for Physics-Informed Neural Networks Via Attentive Representations and Conflict-Resolved Gradients
• Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems
• Physics-informed machine learning for reconstruction of dynamical systems with invariant measure score matching
• PTL-PINNs: Perturbation-Guided Transfer Learning with Physics- Informed Neural Networks for Nonlinear Systems
• Verifying Physics-Informed Neural Network Fidelity using Classical Fisher Information from Differentiable Dynamical System
• Report for NSF Workshop on AI for Electronic Design Automation
• Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
• GlueNN: gluing patchwise analytic solutions with neural networks
• Data-Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning
• NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations
• PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
• Learn and Verify: A Framework for Rigorous Verification of Physics-Informed Neural Networks
• Physics-informed Blind Reconstruction of Dense Fields from Sparse Measurements using Neural Networks with a Differentiable Simulator
• TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
• HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability
• Physics-Informed Neural Networks and Neural Operators for Parametric PDEs
• Discovering Scaling Exponents with Physics-Informed Müntz-Szász Networks
• Unsupervised Physics-Informed Operator Learning through Multi-Stage Curriculum Training
• Putting machine learning to the test in a quantum many-body system
• EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
• PIMPC-GNN: Physics-Informed Multi-Phase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks
• Conformal mapping based Physics-informed neural networks for designing neutral inclusions
• Bayesian Parameter Estimation for Predictive Modeling of Illumination-Dependent Current-Voltage Curves
• Scientific Machine Learning for Resilient EV-Grid Planning and Decision Support Under Extreme Events
• Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations
• First-Principles Optical Descriptors and Hybrid Classical-Quantum Classification of Er-Doped CaF_2_2
• Prototype-based Explainable Neural Networks with Channel-specific Reasoning for Geospatial Learning Tasks
• Validating a Koopman-Quantum Hybrid Paradigm for Diagnostic Denoising of Fusion Devices
• naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement
• HoloHema: Digital Holographic Hematology Analyzer
• Synergizing Kolmogorov-Arnold Networks with Dynamic Adaptive Weighting for High-Frequency and Multi-Scale PDE Solutions
• Learning Hidden Physics and System Parameters with Deep Operator Networks
• Coupled Integral PINN for Discontinuity
• Visualizing the loss landscapes of physics-informed neural networks
• Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing
• A Cycle-Consistent Graph Surrogate for Full-Cycle Left Ventricular Myocardial Biomechanics
• Physics-informed extreme learning machine for Terzaghi consolidation problems and interpretation of coefficient of consolidation based on CPTu data
• A Demonstration of a Neural Network as a Bridge Between Galaxy Simulations and Surveys
• STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
• ODELoRA: Training Low-Rank Adaptation by Solving Ordinary Differential Equations
• FEM-Informed Hypergraph Neural Networks for Efficient Elastoplasticity
• Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks
• Modeling Batch Crystallization under Uncertainty Using Physics-informed Machine Learning
• Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
• Curriculum-Learned Vanishing Stacked Residual PINNs for Hyperbolic PDE State Reconstruction
• Differentiable Modeling for Low-Inertia Grids: Benchmarking PINNs, NODEs, and DP for Identification and Control of SMIB System
• Solving PDEs With Deep Neural Nets under General Boundary Conditions
• Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
• Radial Müntz-Szász Networks: Neural Architectures with Learnable Power Bases for Multidimensional Singularities
• On the Role of Consistency Between Physics and Data in Physics-Informed Neural Networks
• Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives
• Supervised Metric Regularization Through Alternating Optimization for Multi-Regime Physics-Informed Neural Networks
• Drug Release Modeling using Physics-Informed Neural Networks
• Unlearnable phases of matter
• Statistical Learning Analysis of Physics-Informed Neural Networks
• Addressing the ground state of the deuteron by physics-informed neural networks
• Morephy-Net: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Neural Operator Learning Networks
• Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
• Gradient Networks for Universal Magnetic Modeling of Synchronous Machines
• Pseudo-differential-enhanced physics-informed neural networks
• Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs
• Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures
• A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization
• A Unified Benchmark of Physics-Informed Neural Networks and Kolmogorov-Arnold Networks for Ordinary and Partial Differential Equations
• Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems
• Investigating Nonlinear Quenching Effects on Polar Field Buildup in the Sun Using Physics-Informed Neural Networks
• FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
• Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
• Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
• Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
• Discovering Unknown Inverter Governing Equations via Physics-Informed Sparse Machine Learning
• Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine-Learning, and Physics-Informed Methods
• Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
• C3NN-SBI: Learning Hierarchies of NN-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
• Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction
• Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study
• Physics-informed graph neural networks for flow field estimation in carotid arteries
• Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Noise-Stressed Synthetic Conditions
• Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Prediction
• Deepmechanics
• Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
• Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
• Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
• Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
• Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
• Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems
• Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling
• TorchLean: Formalizing Neural Networks in Lean
• Solving stiff dark matter equations via Jacobian Normalization with Physics-Informed Neural Networks
• Astral: training physics-informed neural networks with error majorants
• Hybrid ROM-PINN Framework for Closure Modeling in Convection-Dominated Systems
• Hard-constraint physics-residual networks enable robust extrapolation for hydrogen crossover prediction in PEM water electrolyzers
• PhysFormer: A Physics-Embedded Generative Model for Physically Self-Consistent Spectral Synthesis
• Tackling multiphysics problems via finite element-guided physics-informed operator learning
• Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
• Wireless Power Control Based on Large Language Models
• TENG-BC: Unified Time-Evolving Natural Gradient for Neural PDE Solvers with General Boundary Conditions
• Physics-informed post-processing of stabilized finite element solutions for transient convection-dominated problems
• Stabilized Adaptive Loss and Residual-Based Collocation for Physics-Informed Neural Networks
• Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results
• Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
• Machine Learning the Strong Disorder Renormalization Group Method for Disordered Quantum Spin Chains
• A physics-informed U-Net-LSTM network for nonlinear structural response under seismic excitation
• Improving the accuracy of physics-informed neural networks via last-layer retraining
• Continuous Modal Logical Neural Networks: Modal Reasoning via Stochastic Accessibility
• Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks
• BumpNet: A Sparse MLP Framework for Learning PDE Solutions
• Physics Education under the Application of Artificial Intelligence: Bibliometric Analysis Based on Web of Science Core Library (2021-2025)
• U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach
• Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
• VEGA: Electric Vehicle Navigation Agent via Physics-Informed Neural Operator and Proximal Policy Optimization
• To What Extent Are Star Cluster Ages Encoded in Their Environments? Exploring the Spatial Distribution of Age-Related Information with PHANGS-HST Imaging and Convolutional Neural Networks
• Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets
• Flow Field Reconstruction via Voronoi-Enhanced Physics-Informed Neural Networks with End-to-End Sensor Placement Optimization
• MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
• Enhanced Emittance Evaluation using 2D Transverse Phase Space Distributions, High Resolution Image Denoising, and Deep Learning
• Meta-PINNs: Meta-Learning Enhanced Physics-Informed Machine Learning Framework for Turbomachinery Flow Predictions under Varying Operation Conditions
• Robust Parameter and State Estimation in Multiscale Neuronal Systems Using Physics-Informed Neural Networks
• Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions
• A neural operator for predicting vibration frequency response curves from limited data
• Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
• Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives
• Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization
• Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
• Manifold-Orthogonal Dual-spectrum Extrapolation for Parameterized Physics-Informed Neural Networks
• PolyMon: A Unified Framework for Polymer Property Prediction
• Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask
• Physics-informed neural networks for solving strong-field saddle-point equations in strong-field physics with tailored fields
• Benchmarking Machine Learning Approaches for Polarization Mapping in Ferroelectrics Using 4D-STEM
• Building Trust in PINNs: Error Estimation through Finite Difference Methods
• PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing
• The Evolution of Computer-Assisted Proof In Analysis
• Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
• DustNET: enabling machine learning and AI models of dusty plasmas
• Rapid Neural Network Prediction of Linear Block Copolymer Free Energies
• Physics-informed neural networks for solving saddle-point equations in strong-field physics with tailored fields
• Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data
• Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
• Physics-informed neural network for predicting fatigue life of unirradiated and irradiated austenitic and ferritic/martensitic steels under reactor-relevant conditions
• WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network
• Learning Transferable Friction Models and LuGre Identification Via Physics-Informed Neural Networks
• A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks
• Modeling Inverse Ellipsometry Problem via Flow Matching with a Large-Scale Dataset
• Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
• D_4_4CNN\times\timesAnaCal: Physics-Informed Machine Learning for Accurate and Precise Weak Lensing Shear Estimation
• An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
• Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
• Mathematical Modeling of Cancer-Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
• Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
• Artificial intelligence for partial differential equations in computational mechanics: A review
• Double Coupling Architecture and Training Method for Optimization Problems of Differential Algebraic Equations with Parameters
• Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks
• BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
• Many-body mobility edges in one dimension revealed by efficient and interpretable feature-based learning with Kolmogorov-Arnold Networks
• SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
• Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach
• Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
• Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
• An efficient wavelet-based physics-informed neural network for multiscale problems
• Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
• Resolving gradient pathology in physics-informed epidemiological models
• Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
• Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schrödinger Equation
• Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions
• How unconstrained machine-learning models learn physical symmetries
• Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory
• Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
• Physics-Guided Transformer (PGT): Physics-Aware Attention Mechanism for PINNs
• Improving ideal MHD equilibrium accuracy with physics-informed neural networks
• Comparing Physics-Informed and Neural ODE Approaches for Modeling Nonlinear Biological Systems: A Case Study Based on the Morris-Lecar Model
• A Comparative Investigation of Thermodynamic Structure-Informed Neural Networks
• Determining the NJL Coupling and AMM in Magnetized QCD Matter via Machine Learning
• From Physics to Surrogate Intelligence: A Unified Electro-Thermo-Optimization Framework for TSV Networks
• Lie Generator Networks for Nonlinear Partial Differential Equations
• A Unified Weighted-Loss Physics-Informed Neural Network for Boundary Layer Problems in Singularly Perturbed PDEs
• Biomimetic PINNs for Cell-Induced Phase Transitions: UQ-R3 Sampling with Causal Gating
• From Astronomy to Astrology: Testing the Illusion of Zodiac-Based Personality Prediction with Machine Learning
• ELM-FBPINNs: An Efficient Multilevel Random Feature Method
• A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations
• Revisiting Conservativeness in Fluid Dynamics: Failure of Non-Conservative PINNs and a Path-Integral Remedy
• Experimental Design for Missing Physics
• A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
• WGFINNs: Weak formulation-based GENERIC formalism informed neural networks'
• PINNs in PDE Constrained Optimal Control Problems: Direct vs Indirect Methods
• Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques
• Machine-learning based flow field estimation using floating sensor locations
• A Pedagogical Framework for Physics-Informed Machine Learning: From Classical Pendulum to Quantum Anharmonic Oscillator Using PyTorch on Modern GPU Hardware
• General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
• Quantum Machine Learning for particle scattering entanglement classification
• WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
• Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
• Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine Learning, and Physics-Informed Methods
• Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
• A Theory-guided Weighted L^2L^2 Loss for solving the BGK model via Physics-informed neural networks
• A Texture-Generalizable Deep Material Network via Orientation-Aware Interaction Learning for Polycrystal Modeling and Texture Evolution
• Biomimetic causal learning for microstructure-forming phase transitions
• Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
• Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
• Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing
• Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
• A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
• Quantum Measurement Statistics as Bayesian Uncertainty Estimators for Physics-Constrained Learning
• Battery health prognosis using Physics-informed neural network with Quantum Feature mapping
• Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis
• Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System
• Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
• Hardware-Efficient Neuro-Symbolic Networks with the Exp-Minus-Log Operator
• Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
• Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
• Outperforming Self-Attention Mechanisms in Solar Irradiance Forecasting via Physics-Guided Neural Networks
• Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator
• Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks
• SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
• Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
• Auxiliary Finite-Difference Residual-Gradient Regularization for PINNs
• Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
• PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs
• Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
• Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
• Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
• Hybrid Multi-Dimensional MRI Prostate Cancer Detection via Hadamard Network-Based Bias Correction and Residual Networks
• Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
• Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
• Benchmarking Physics-Informed Neural Networks and Boundary Elements Methods for Wave Scattering
• Physics-Informed Neural Networks for Biological 2\mathrm{D}{+}t2\mathrm{D}{+}t Reaction-Diffusion Systems
• Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle
• Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
• Analytical and Machine Learning Methods for Model Discernment at CEννNS Experiments
• Transferable Physics-Informed Representations via Closed-Form Head Adaptation
• A Green-Integral-Constrained Neural Solver with Stochastic Physics-Informed Regularization
• Multi-scale Dynamic Wake Modeling of Floating Offshore Wind Turbines via Fourier Neural Operators and Physics-Informed Neural Networks
• Fixed-Reservoir vs Variational Quantum Architectures for Chaotic Dynamics: Benchmarking QRC and QPINN on the Lorenz System
• When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions
• Advantages of Broadband Metalenses for Generalizable Image Classification
• Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen
• Computational Control of Nonlinear Partial Differential Equations Using Machine Learning
• Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos
• Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
• Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching
• Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeam
• Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
• An adaptive wavelet-based PINN for problems with localized high-magnitude source
• An approach to encode divergence-free stress fields in neural approximations based on stress potentials
• PILIR: Physics-Informed Local Implicit Representation
• TRACED: In vivo imaging of extracellular intrinsic diffusivity, tortuosity, cell size distribution and cell density in human glioma patients
• Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics
• Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
• Geometric Quantum Physics Informed Neural Network
• Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
• Constructing Inverse Potentials from Scattering Phase Shifts using Physics-Informed Neural Networks: Application to Neutron-Alpha Scattering
• Chebyshev-Augmented One-Shot Transfer Learning for PINNs on Nonlinear Differential Equations
• Random test functions, H^{-1}H^{-1} norm equivalence, and stochastic variational physics-informed neural networks
• Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
• Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
• Information in Many-body Eigenstates: A Question of Learnability
• Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques
• Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
• Gradient Scaling Effects in Adaptive Spectral PINNs for Stiff Nonlinear ODEs
• INEUS: Iterative Neural Solver for High-Dimensional PIDEs
• Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions
• Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks
• Sensoformer: Robust Sim-to-Real Inference on Variable-Geometry Sensor Sets via Physics-Structured Randomization
• Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
• Pre-training Enables Extraordinary All-optical Image Denoising
• Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction
• Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning
• Functional-prior-based Bayesian PDE-constrained inversion using PINNs
• SymTorch: Symbolic Distillation of Neural Networks
• DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling
• Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
• Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
• Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
• Bayesian Reasoning for Physics Informed Neural Networks
• Inferring identified hadron production in pppp collisions with physics-informed machine learning at the LHC
• Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
• A Deep Risk Estimator for Known Operator Learning
• AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
• When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
• Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
• Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
• Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
• Finite Volume-Informed Neural Network Framework for 2D Shallow Water Equations: Rugged Loss Landscapes and the Importance of Data Guidance
• Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
• Unified generalization analysis for physics informed neural networks
• NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
• Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
• Unsupervised simulation of incompressible flows with physics- and equality- constrained artificial neural networks
• Unbiased and Second-Order-Free Training for High-Dimensional PDEs
• Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
• A numerical study into neural network surrogate model performance for uncertainty propagation
• Variational Autoregressive Networks with probability priors
• When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
• Microscopic derivation of the interacting boson model parameters with machine learning
• Deep Learning Alternatives of the Kolmogorov Superposition Theorem
• Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation
• A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering
• Stochastic Penalty-Barrier Methods for Constrained Machine Learning
• StableGrad: Backward Scale Control without Batch Normalization
• From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
• Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
• Physics-informed convolutional neural networks for fluid flow through porous media
• MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy
• Holomorphic Neural ODEs with Kolmogorov-Arnold Networks for Interpretable Discovery of Complex Dynamics
• Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
• Learning partially observed systems with neural Hamiltonian ordinary differential equations
• Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
• Physics-informed sparse identification-based tube model predictive control for aerial vehicles
• 3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks
• Mitigating Gradient Pathology in PINNs through Aligned Constraint
• Fourier Feature Pyramids for Physics-Informed Neural Networks
• Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem
• Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
• Minimal surfaces, Knots, and Neural Networks
• Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities
• Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
• Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model
• IGA-ODIL: Optimizing DIscretre robust Loss with Isogeometric Analysis to solve forward and inverse problems faster using machine learning tools
• Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation
• A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
• Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
• An End-to-End PyTorch Interface for Differentiable PDE Solvers: A RANS Model-Correction Study
• A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials
• Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
• Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
• PINNs Failure Modes are Overfitting
• Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies
• Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
• MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning
• Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
• Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
• Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
• PINNfluence: Interpreting PINNs through Influence Functions
• MAdam: Metric-Aware Multi-Objective Adam
• Lagrangian-Eulerian learning of flow field and trajectories with TrajectoryFlowNet
• Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
• Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers
• Curvature-aware dynamic precision approach for physics-informed neural networks
• Loss-Conditional PINNs for Parametric PDE Families
• SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
• DAS-PINNs for high-dimensional partial differential equations: extending deep adaptive sampling to spacetime domains
• Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
• Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks
• On the training of physics-informed neural operators for solving parametric partial differential equations
• On the Effect of Neural Field Reparameterization for 4DVAR
• Stochastic Dimension Implicit Functional Projections for Global Integral Conservation in High-Dimensional PINNs
• Sound Field Interpolation Using Physics-Informed Extreme Learning Machine with Pre-Training
• Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
• Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks
• An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks
• PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection
• Accelerating SAV-based optimization via randomized low-rank Hessian approximation
• Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
• Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems
• Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems
• Transferable Machine Learning of Electronic Hamiltonians with Superposition-of-Atomic-Potentials Features
• Reliable Error Estimation for PINNs: Lower and Upper A Posteriori Bounds
• DSpinGNN: A Physics-Informed Equivariant Graph Neural Network for Dynamic Magnetic Exchange Prediction in Strain-Deformed Monolayer CrI_3_3
• Loss Landscape Diagnosis for Gradient-Based Gray-Scott System Inversion: Disentangling the Roles of PINN Components
• Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources
• Physics-Informed Neural Networks for Chemotherapy Pharmacokinetics: Benchmarking the Clinical Estimator and Exposing Parameter Identifiability
• Robin-Neumann Coupling of PINN and FEM Solvers: A Steklov-Poincaré View, with Application to Fluid-Structure Interaction with Contact
• RepNet: Tackling spectral bias in deep neural networks via parameter reparameterization
• Petrov-Galerkin Variational Physics-Informed Neural Network Framework for Two-Dimensional Singularly Perturbed Problems
• Neural network inverse design of nanophotonic scintillators
• Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System
• A Koopman-PINN Framework for Epidemic Models: Parameter Inference and Forecasting
• Random Gradient-Free Optimization in Infinite Dimensional Spaces
• A Convex Quasilinearization Method for Solving Nonlinear PDEs with Physics-Informed Neural Networks
• INI-VPINN: A Variational Physics-Informed Neural Network with Implicit Neumann and Interface Handling for Multi-Material Domains with Geometric Singularities
• Implicit Neural Representations Framework for One-Dimensional Magnetotelluric Inversion
• Estimating carbon pools in the European Shelf sea environment: replacing reanalysis by model-informed machine learning?
• A Neural Network Framework for Geodesic-Like Curve Computation on Parametric Surfaces
• Trainable Photonic Measurement for Physics-Informed PDE Learning
• Enhancing neural network extrapolation in thermo-fluid systems using steady-state solutions
• Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
• Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs
• RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization
• Physics-Informed Neural Network with Squeeze-Excitation-like Attention
• Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System
• Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
• A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling
• Neural Architectures as Functional Priors in Physics-Informed Control Problems
• Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
• Physics-Informed Neural Networks for Computing the Morse Index of the Critical Catenoid
• Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
• Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection
• Extended pseudo-spectral physics-informed neural networks for phase-field models
• It's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks
• Physics-Informed Modeling for Wood Thermal Analysis and Prediction
• Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving
• Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification
• Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation
• Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte
• Recovering Sharp Conductivity Features in the Finite-Data Calderón Problem with Physics-Informed Neural Networks
• LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries
• Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua
• Hard-constraint physics-residual networks for hydrogen crossover prediction and high-pressure extrapolation in PEM water electrolysis
• Evaluating Newtonian Mechanics in Video Generative Models with Real Physical Systems
• On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks
• Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flows
• On-Demand Coherent Nanolaser Metalens and Beam Steering Enabled by Physics-Informed Neural Networks
• Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
• An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
• Frequency Shift Physics-Informed Extreme Learning Machine for Solving High-Frequency Partial Differential Equations
• Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
• LRX-PINN: A Layer-Resolving XNet Physics-Informed Neural Network with Integrated Cauchy Activations for Convection-Dominated Problems
• Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks for the Poisson Equation
• Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
• Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
• Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach
• Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates
• Machine Learning Materials Properties by Encoding Orbital-Projected Density of States
• The Generalization Gap in Machine Learning EoS Inference from Core-Collapse Supernova Gravitational Waves
• Inertia-Aware Optimal Power Flow Using PINN in IBR-Dominated Power Systems
• SPARC-Net: A Spectral, Causality-Aware, and Hard-Constrained Physics-Informed Architecture for Stiff and Shock-Dominated Partial Differential Equations
• Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks
• SpectraSensML Software: Mastering Complete Spectral Information for Luminescence Thermometry 2.0
• The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
• The Differential Neural Tangent Kernel and Its Positivity
• Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials
• Constraining the High-Density Equation of State with Present and Future NICER Observations Using Physics-Informed Regularized Machine Learning
• PolarBM: Complex-valued Boltzmann Machine for Modeling Audio Signals in Polar and Log-polar Coordinates
• Advanced Techniques in Stability Analysis of Trans-Neptunian Objects
• Spectral-Informed Neural Networks Outperform Spectral Methods in High-dimensional PDEs
• Probabilistic Physics-Informed Neural Networks for Estimating Heterogeneous Elastic Properties from Low-Resolution and Noisy Displacement Data
• LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks
• Trainable Spline Representations for Physics-Informed Learning
• FlashPDE: A Drop-in Fused Triton Operator Library for Neural PDE Solvers
• Stochastic Dimension Zeroth-Order Estimator: Stable and Memory-Efficient Training of PINNs
• Neural operator preconditioning from mixed dataset for the Helmholtz equations: Application to transcranial ultrasound
• Boundary-Adapted PINNs for Elliptic Dirichlet Problems: H^2(Ω)H^2(Ω) A Priori Error Bounds with Application to Mean Escape Time Computation
• Reliability-Aware Hard--Soft Physics-Informed Neural Networks for Robust Learning of Challenging Partial Differential Equations
• FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers
• Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery
• A Graph Neural Network approach to zero-shot Digital Twins
• Latent PDE mapping for efficient physics-informed learning across geometries with limited data
• Global Convergence of DGM and PINN Algorithms for Solving Nonlinear PDEs
• Variational Boosting for Physics-Informed Neural Networks
• Physics-Informed Neural Networks for Predicting Nitrous Oxide Flux
• Physics-Informed Neural Networks for Discovering Periodic Orbits in the Gravitational Three-Body Problem
• Neural Representation of Minimal Surfaces
• Performance of Krotov, PRONTO and PINN for optimal control of quantum gates
• Stochastic Dynamics of the Two-Dimensional Low-to-High Transition System Driven by Multiplicative Noise
• Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equations
• PIKS: Universal Physics-Informed Kernel Methods
• Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow
• EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
• Data-free neural PDE solvers based on Graph Neural Networks and weak forms
• Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries
• Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate
• Physics-structured cooperative neural network for baseline-free nuclear mass modeling
• Feature Interaction Modeling for Physics-Informed Neural Networks and Neural Operators
• A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem
• Deep Learning-Based Classification and Analysis of Pulsar Candidates in Fermi-LAT Unassociated Sources
• Perspectives on Tsallis Statistics for Artificial Intelligence
• Physics-informed neural networks for two-dimensional wall-reactive solute dispersion in canonical shear flows
• Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics
• An Embedded RISC-V Evaluation of Kolmogorov--Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models
• Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations
• A 6G Integrated Sensing and Communication Framework for Railway Intrusion Detection and Collision Prediction
• The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
• From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs
• Alternating Levenberg-Marquardt Training of Physics-Informed Neural Networks with Fourier-Enhanced Features
• Entangling power of neural networks
• Classifying Multipartite Continuous Variable Entanglement Structures through Data-augmented Neural Networks
• Eikonal Regularisation in Physics-Informed Neural Networks for Three-Dimensional Level-Set Advection: Transferability of Two-Dimensional Design Principles
• Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework
• Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains
• Nonadiabatic Molecular Dynamics on Real-time Excited-State Surfaces via Machine Learning Hamiltonians
• Tools to Explain Neural Networks for Power System Dynamics
• PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention
• Efficient Weak-Entropy PINN for Solving Hyperbolic Conservation Laws
• The near-threshold cross section of e^+e^- \to Ω^-\barΩ^+e^+e^- \to Ω^-\barΩ^+: Heavy-flavor rescattering and physics-informed deep learning
• Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data
• Hierarchical rank-evolving representation for physics-informed neural networks
• Robust Fault Detection and Classification in Power Systems via Physics-Informed and Data-Driven Learning
• ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks
• Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis
• Derivative Computation in PINNs: Automatic Differentiation, Finite Differences and Beyond
• Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification
• HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks
• Beyond Field Accuracy: Two-Axis Diagnosis of Inverse-PINN Parameter Error
• MiNO: Cotangent-bundle propagator learning for PDEs
• ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning
• Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks
• Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data
• Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
• Deep-learning classification of physically admissible nuclear-matter equations of state
• Complementary, Not Cumulative: Interaction Effects in Physics-Informed Neural Networks for Navier-Stokes Vortex Shedding
• Residual-based attention in physics-informed neural networks
• Learning in PINNs: Phase transition, diffusion equilibrium, and generalization
• Precise Modeling of a Complex Solenoidal Magnetic Field Using a Combination of Analytic Functions and a PINN
• Robust training and rigorous error analysis of physics-informed neural networks for the pp-Laplace equation
• Extensive Air Showers Parameters Estimation Using Machine Learning Techniques with Simulations of the FAST Telescope
• StablePDENet: Enhancing Neural Operator Stability through Physics-Informed Residual-Sensitivity Regularization
• Physics-Informed Foresight Pruning for Sparse PINN Solvers of Nonlinear PDEs
• Joint Initialization of Flux Networks and Effective Multiplication Factor for Physics-Informed Neural Networks Solving Neutron Diffusion Problems
• Neither Precision Nor Architecture Alone: Controlled Tests of Failure Remedies for Physics-Informed Neural Networks
• Physics-Informed Error Field Learning: A Post-Training Optimization Framework for Physics-Informed Neural Networks
• When Does Frequency Decomposition Benefit Physics-Informed Neural Networks? A Preliminary Ablation Study
• Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training for Nonlinear Differential Equations
• Beyond Electrons: A Theoretical Framework for Near-Field Radiative Thermal Computing and Neural-Network-Inspired Processing
• Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots
• QGPINNs: A Physics-Informed Neural Network Framework for Nonlocal Differential Equations on Quantum Graphs
• Application of the latent twins approach for clear sky retrieval from IASI observations
• Learning dynamically consistent flow reconstructions from limited observations
• SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning
• SDF-Aware Weighting: Adaptive Eikonal Regularisation for Three-Dimensional Level-Set Physics-Informed Neural Networks
• Gradient-Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks
• Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks
• A Computational Comparison of Fourier Spectral Differentiation and Spatial Automatic Differentiation in Periodic Physics-Informed Neural Networks
• Linearized subspace refinement framework to expose hidden accuracy in trained neural networks
• Observation-Aligned Two-Stage Domain Decomposition for Physics-Informed Traffic State Estimation with Sparse Fixed Sensors
• Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial \mathrm{SrTiO_3}\mathrm{SrTiO_3} on Si memristors via Dynamic Spectral Optimization
• Beyond Residuals: Energy based solutions of partial differential equations using scientific machine learning
• Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment
• Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics
• A Systematic Analysis of Automatic Differentiation versus Discretization-based Constraints for Physics-Informed PDE Solvers
• Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks
• PhysSAE: Mechanistic Interpretability with Sparse Autoencoders
• In situ local learning of dynamic network materials
• Gaussian Linear Functional Manifold Method for Massive Point Cloud Data
• Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography
• Development and Validation of a Physics-Guided Machine Learning Extrapolation Framework Using a Classical Transient Diffusion Benchmark
• A variational physics-informed graph neural network for heterogeneous solid mechanics
• Physics-enriched neural solvers for transient ice-flow simulation
• PDE-constrained inverse problems at the \sqrt{n}\sqrt{n} rate via debiased physics-informed neural networks
• Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
• Architecture--Optimization Co-Design for Physics-Informed Neural Networks via Layer-wise Coordinate Adaptation and Gradient Conflict Resolution
• PhysSAE: Mechanistic Interpretability of PINNs with Sparse Autoencoders
• A pullback-corrected scalar auxiliary variable optimizer with momentum and adaptive mobility
• Physics Informed Random Feature Neural Networks for Solving PDEs
• Deep learning emergent spacetime from fermionic spectral functions in holography
• Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra
• Hybrid coupling with numerics-informed neural networks and the overlapping Schwarz alternating method
• Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications
• Beyond PINNs: A Unified Gauss--Newton and Petrov--Galerkin Framework for Neural and Hybrid PDE Solvers
• Comparison of Image Processing Models in Quark Gluon Jet Classification
• Amortizing Physics-Informed Neural Solvers via Graph Hypernetworks
• Uncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference
• Normalizing Flow-Based Bayesian Parameter Estimation for Noisy Quantum States
• Learn2Drive: A neural network-based framework for socially compliant automated vehicle control
• Automated Physics-Informed Neural-Networks-Based Calibration of Highly Segmented Silicon Telescopes
• An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location
• Learning Physics from an Imperfect Ancestor
• A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
• From Local Atomic Motifs to Thermodynamic State: An Interpretable Physics-Informed Framework for Cu-Zr Metallic Glasses
• LPINNs: First-Layer Gated Localization for Physics-Informed Neural Networks
• Label Propagation for Physics-Informed Neural Networks and Physics-Informed Gaussian Processes
• Approaching the Inverse Neutron Scattering Problem with Neural Networks
• AURA: Angular Update Rate Adaptation for training complex-valued neural networks
• Sex Estimation from Footwear Outsole Impressions Using CNN Transfer Learning and Interpretable Image Statistics
• Data-driven discrete-time deep recurrent neural network-based modeling for dissipative systems
• An improved periodic activation for PINNs reconstructing convective flows
• Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior
• The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
• NEXT: Physics-Informed Neuro-Spectral Exponential Time Differencing Architectures
• Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection
• Gradient Surgery for Physics-Informed Neural Networks
• Deep-Learning Solvers and Surrogates for Infinity and p-Laplace Problems
• RepNN: Tackling spectral bias in deep neural networks for regression and PDE problems via parameter reparameterization
• Physics-Informed Neural Networks for Depth-Averaged Avalanche Dynamics
• From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?
• The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning
• Machine learning for the LHC physics program: a 2025-2026 stocktake
• PINNMorph: Evolving Online Adaptation Policies for Physics-Informed Neural Networks
• Cool Embeddings: Predicting Galaxy Cluster Cooling Times in IllustrisTNG and TNG-Cluster with AstroCLIP
• Uncovering flame physics with machine learning: application to the reaction rate in hydrogen flames
• Convex Physics Informed Neural Networks for the Monge-Ampère Optimal Transport Problem
• Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
• CI-PINN: Causal Integral Physics-Informed Neural Network for Solving Evolution Equations
• Tensor-Train Compressed Separable PINNs: A Curvature-Aware Optimization Framework for Parametric PDEs in High Dimensions
• KernelOnet: An Interpretable Neural Operator Based on Kernel Functions
• Wrong Operator or Blind Design? A Reference-Free Diagnostic for Physics-Informed Coefficient Learning
• Towards solving General Relativity with Physics-Informed Neural Networks
• SoftServe: A Scalable Quasi-Newton Method for Deep Learning
• Kolmogorov-Arnold Networks for Free-Boundary Partial Differential Equations
• Fourier Symmetrization for Geometric Quantum Machine Learning
• Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
• Operator-informed initialization for Fourier features physics-informed neural networks
• S^{2}^{2}-PINN: Stochastic Separable Physics-Informed Neural Networks
• Hyperparameter selection for equation learning with biologically-informed neural networks
• Evaluation of Sampling Strategies and Physics-Informed Kolmogorov--Arnold Networks in Unbounded Domains
• Feature tracking in physics-informed neural networks via joint optimization of nonlinear deformation manifolds: application to shocks
• IGA-KAN: Isogeometric Analysis with Physics-Informed Closed-Form Kolmogorov-Arnold Networks for Forward and Inverse PDEs
• Comparison of neural and spline representations for physics-informed learning
• Parameter Estimation in Machining Dynamics with Regenerative Delay and Nonsmooth Friction using Physics-Informed Neural Networks
• Boosted top tagging via lepton-in-jet topology for vector-like quark searches in FCC-hh
• HFS-TransNet: A Hybrid Fixed-Stress Transferable Neural Network for Quasi-Static Biot Poroelasticity
• CEENs: Causality-enforced evolutional networks for solving time-dependent partial differential equations
• Checkable NTK Positivity and Finite-Width Gradient Descent for Scalar- and Vector-Valued PINNs with Strong-Form, Weak-Form, and Nonlocal Linear Constraints
• Stochastic Penalty-Barrier Method for Constrained Machine Learning
• Soft Contrastive Learning for Unsupervised Discovery of Phases of Matter
• Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata
• Domain-informed Adaptive Sampling for Generalizable PINNs in Metal Additive Manufacturing via Conditional Flow Matching
• Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation
• Uncovering and Fixing Collider Bias in Bayesian PINNs
• Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries
• Power Side-Channel Membership Inference Attack on Embedded Machine Learning
• Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations
• A mesh-based neural energy method for the simulation of heterogeneous composites
• NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
• A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
• PDE-PFN: Prior-Data Fitted Neural PDE Solver

Library

Tutorial

Written Tutorials

Video Tutorials

• Physics Informed Neural Networks (PINNs) (Steve Brunton) — A comprehensive introduction to PINN architecture and its role in machine learning
• Machine Learning for Computational Fluid Dynamics (Steve Brunton) — Deep dive into using neural networks for scientific computing and fluid simulations
• Data-driven Model Discovery (Nathan Kutz) — Explores targeted use of deep neural networks for advanced physics and engineering applications
• Accelerating FEM with ML (Legato Team) — Intro to Finite Element Neural Networks and how ML accelerates traditional physics modeling

Contributing

We welcome contributions to this repository! If you have a resource that you believe should be included, please submit a pull request or open an issue. Contributions can include:

• New libraries or tools related to PIML or PINNs
• Tutorials or guides that help users understand and implement PIML techniques
• Research papers that advance the field of PIML or PINNs
• Any other resources that you find valuable for the community

How to Contribute

1. Fork the repository.
2. Create a new branch for your changes.
3. Make your changes and commit them with a clear message.
4. Push your changes to your forked repository.
5. Submit a pull request to the main repository.

Before contributing, take a look at the existing resources to avoid duplicates.

License

This repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material, provided you give appropriate credit, link to the license, and indicate if changes were made.

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