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.
April 18, 2026 at 02:08:31 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
- 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
- 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
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
- Fork the repository.
- Create a new branch for your changes.
- Make your changes and commit them with a clear message.
- Push your changes to your forked repository.
- 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.