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.

@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.

December 25, 2025 at 01:25:41 AM UTC

• 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

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

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.

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.