Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

2018/11/03 by Maziar Raissi, M. Raissi, P. Perdikaris +3 · 1091 citations
Physics and Astronomy · Engineering · Earth and Planetary Sciences · #Model Reduction and Neural Networks #Fluid Dynamics and Turbulent Flows #Meteorological Phenomena and Simulations

paper · doi:10.1016/j.jcp.2018.10.045

Citations

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• Physics informed deep learning for computational elastodynamics without labeled data
• Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems
• Adaptive evolution enhanced physics-informed neural networks for time-variant health prognosis of lithium-ion batteries
• When and why PINNs fail to train: A neural tangent kernel perspective
• Forward and Inverse Mantle Convection with Neural Operators
• xLSTM-PINN: Memory-Gated Spectral Remodeling for Physics-Informed Learning
• Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation
• Real-Time Physics-Aware Battery Health Monitoring from Partial Charging Profiles via Physics-Informed Neural Networks
• An Adjoint Formulation of Energetic Particle Confinement
• The modified Physics-Informed Hybrid Parallel Kolmogorov--Arnold and Multilayer Perceptron Architecture with domain decomposition
• PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI
• IDOL: Meeting Diverse Distribution Shifts with Prior Physics for Tropical Cyclone Multi-Task Estimation
• A Deep Learning Density Shaping Model Predictive Gust Load Alleviation Control of a Compliant Wing Subjected to Atmospheric Turbulence
• Convergent series of Stokes wave of arbitrary height in deep water via machine learning
• Guaranteeing Conservation of Integrals with Projection in 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
• The curse of dimensionality: what lies beyond the capabilities of physics-informed neural networks
• Orthogonal-by-construction augmentation of physics-based input-output models
• Implicit Neural Representations with Periodic Activation Functions
• Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework
• NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos
• Numerical Approaches for Identifying the Time-Dependent Potential Coefficient in the Diffusion Equation
• Physics-Informed Neural Operators for Cardiac Electrophysiology
• Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
• Scalable Unidirectional Pareto Optimality for Multi-Task Learning with\n Constraints
• Modelling of Flow Past Long Cylindrical Structures
• Modeling Unsteady Aircraft Aerodynamics Using Lorenz Attractor: A Reduced-Order Approach for Wing Rock
• Walsh-Hadamard Neural Operators for Solving PDEs with Discontinuous Coefficients
• AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning
• Toward fast, accurate and robust AI prediction of ground states in rotating BEC
• DeepRWCap: Neural-Guided Random-Walk Capacitance Solver for IC Design
• Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
• Physically-Grounded Goal Imagination: Physics-Informed Variational Autoencoder for Self-Supervised Reinforcement Learning
• Nemytskii neural operator: a nonlinear model reduction method for parametrized partial differential equations
• Spectrum and Physics-Informed Neural Networks (SaPINNs) for Input-State-Parameter Estimation in Dynamic Systems Subjected to Natural Hazards-Induced Excitation
• From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning
• A Classical-Quantum Hybrid Architecture for Physics-Informed Neural Networks
• Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field
• Learning the Inverse Ryu--Takayanagi Formula with Transformers
• Mathematical Analysis and Modeling of Ebola Virus Dynamics via Optimal Control and Neural Network Paradigms
• Enhancing PINN Accuracy for the RLW Equation: Adaptive and Conservative Approaches
• Event-driven physics-informed operator learning for reliability analysis
• NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
• Physics-Informed Design of Input Convex Neural Networks for Consistency Optimal Transport Flow Matching
• Learning solutions of parameterized stiff ODEs using Gaussian processes
• Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation
• Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)
• Precipitation nowcasting of satellite data using physically-aligned neural networks
• Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
• Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
• Uniformly accurate structure-preserving neural surrogates for radiative transfer
• Self-adaptive weighting and sampling for physics-informed neural networks
• An asymptotic stability proof and a port-Hamiltonian physics-informed neural network approach to chaotic synchronization
• Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
• Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity
• Accelerating scientific discovery with the common task framework
• Autoencoding Dynamics: Topological Limitations and Capabilities
• Depth-induced NTK: Bridging Over-parameterized Neural Networks and Deep Neural Kernels
• A unified physics-informed generative operator framework for general inverse problems
• Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics
• Hybrid DeepONet Surrogates for Multiphase Flow in Porous Media
• Prevention is Better than Cure: Handling Basis Collapse and Transparency in Dense Networks
• In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization
• Condition Numbers and Eigenvalue Spectra of Shallow Networks on Spheres
• DAMBench: A Multi-Modal Benchmark for Deep Learning-based Atmospheric Data Assimilation
• A Novel Reservoir Computing Framework for Chaotic Time Series Prediction Using Time Delay Embedding and Random Fourier Features
• A physics-augmented neural network framework for finite strain incompressible viscoelasticity
• Energy Loss Functions for Physical Systems
• HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs
• Neural Green's Functions
• Fast PINN Eigensolvers via Biconvex Reformulation
• Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology
• FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Prediction
• Physics-Informed Neural Networks for Speech Production
• Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problems
• Approximating Young Measures With Deep Neural Networks
• What Can One Expect When Solving PDEs Using Shallow Neural Networks?
• Data-Augmented Deep Learning for Downhole Depth Sensing and Field Validation
• Solving Infinite-Horizon Optimal Control Problems using the Extreme Theory of Functional Connections
• Exact Terminal Condition Neural Network for American Option Pricing Based on the Black-Scholes-Merton Equations
• Learning Soft Robotic Dynamics with Active Exploration
• Finite Element Representation Network (FERN) for Operator Learning with a Localized Trainable Basis
• Generative sampling with physics-informed kernels
• Incorporating Local Hölder Regularity into PINNs for Solving Elliptic PDEs
• Continuous subsurface property retrieval from sparse radar observations using physics informed neural networks
• Learning Soil Physics from Partial Knowledge and Data: Partitioning Capillary and Adsorbed Soil Water
• Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning
• Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
• Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models
• Equation Discovery, Parametric Simulation, and Optimization Using the Physics-Informed Neural Network (PINN) Method for the Heat Conduction Problem
• Meshless solutions of PDE inverse problems on irregular geometries
• LieSolver: A PDE-constrained solver for IBVPs using Lie symmetries
• Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
• Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
• Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equations
• Measurement of multiple mechanical properties from multi-dimensional signals in nanosecond laser ablation via PINN
• IS-PINN: Integrating mathematical principles to solve inhomogeneous wave equations via physics-informed neural networks
• Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
• Deep Multi-Fidelity Active Learning of High-dimensional Outputs
• Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
• Multi-dimensional photodetection: from material intrinsic properties and metasurface engineering to silicon photonic integration
• FINCHES: A Computational Framework for Predicting Intermolecular Interactions in Intrinsically Disordered Proteins
• Physics-Informed Machine Learning Simulator for Wildfire Propagation
• E-STGCN: extreme spatio-temporal graph convolutional networks for air quality forecasting
• Inverse Estimation of Elastic Modulus Using Physics-Informed Generative\n Adversarial Networks
• Multipole Graph Neural Operator for Parametric Partial Differential Equations
• Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
• A universal deep learning strategy for designing high-quality-factor photonic resonances
• EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
• Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling
• Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models
• Dimensional Type Systems and Deterministic Memory Management: Design-Time Semantic Preservation in Native Compilation
• Darboux transformation-based LPNN generating novel localized wave solutions
• Fold bifurcation identification through scientific machine learning
• Deep Learning in Protein Structural Modeling and Design
• Solving singularly perturbed eigenvalue problems via TFPM-Inspired neural networks
• TI-PINN: Topological-identifier physics-informed neural networks for forward and inverse problems of discontinuous coefficient differential equations
• Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework
• A robust and stable hybrid neural network/finite element method for 2D flows that generalizes to different geometries
• Error estimates of residual minimization using neural networks for linear PDEs
• Magnetic, thermal and rotational evolution of isolated neutron stars
• AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
• A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
• Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI
• A transformer-based neural operator for large-eddy simulation of turbulence
• Committor functions via tensor networks
• Uncertainty-oriented physics-informed long short-term memory (UOPI-LSTM) network framework for dynamic force identification with interval uncertainties
• Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators
• Learning Size and Shape of Calabi-Yau Spaces
• DPM: A deep learning PDE augmentation method (with application to\n large-eddy simulation)
• Digital Twin: Values, Challenges and Enablers
• Enhancing Training of Physics-Informed Neural Networks Using Domain Decomposition–Based Preconditioning Strategies
• fPINNs: Fractional Physics-Informed Neural Networks
• Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach
• Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
• Neural Fields in Visual Computing and Beyond
• Physics-constrained neural networks for half-space seismic wave modeling
• Linearly Constrained Neural Networks
• PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
• Solving and Learning Nonlinear PDEs with Gaussian Processes
• Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction
• Solving the linear transport equation by a deep neural network approach
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• The Hard-Constraint PINNs for Interface Optimal Control Problems
• From limited observations to the state of turbulence: Fundamental\n difficulties of flow reconstruction
• Learning Developmental Scaffoldings to Guide Self-Organisation
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• MIM: A deep mixed residual method for solving high-order partial differential equations
• Data-driven optimal control of a SEIR model for COVID-19
• Multiresolution Convolutional Autoencoders
• Solving Physics Olympiad via Reinforcement Learning on Physics Simulators
• Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver
• Learning collision operators from plasma phase space data using differentiable simulators
• Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows
• Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
• Developing a novel structured mesh generation method based on deep neural networks
• Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
• Enforcing boundary conditions for physics-informed neural operators
• HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
• Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation
• A data-driven multiscale scheme for anisotropic finite strain magneto-elasticity
• EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
• UniField: Joint Multi-Domain Training for Universal Surface Pressure Modeling
• Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
• Efficient Global-Local Fusion Sampling for Physics-Informed Neural Networks
• A Practitioner's Guide to Kolmogorov-Arnold Networks
• Auto-Adaptive PINNs with Applications to Phase Transitions
• Reanimating the past: From historical collections of the placenta and uterus to modern imaging, machine learning, and multiscale modeling
• Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
• Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network
• Deeply Learned Spectral Total Variation Decomposition
• Discovery of interpretable patterning rules by integrating mechanistic modeling and deep learning
• Training Deep Physics-Informed Kolmogorov-Arnold Networks
• Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
• Complexity Dependent Error Rates for Physics-informed Statistical Learning via the Small-ball Method
• DeepSalt: Bridging Laboratory and Satellite Spectra through Domain Adaptation and Knowledge Distillation for Large-Scale Soil Salinity Estimation
• Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN) for Spaghetti Bridge Load Prediction
• Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data
• Numerical Spectrum Linking: Identification of Governing PDE via Koopman-Chebyshev Approximation
• Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity
• An uncertainty-aware physics-informed neural network solution for the Black-Scholes equation: a novel framework for option pricing
• Self-induced stochastic resonance: A physics-informed machine learning approach
• Multi-Scale Finite Expression Method for PDEs with Oscillatory Solutions on Complex Domains
• HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems
• Dynamic Graph Neural Network for Data-Driven Physiologically Based Pharmacokinetic Modeling
• A Hybrid GNN-LSE Method for Fast, Robust, and Physically-Consistent AC Power Flow
• PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
• Sparse surface pressure-based reconstruction of the flow around a thick airfoil over a range of angles of attack
• Portfolio selection with exogenous and endogenous transaction costs under a two-factor stochastic volatility model
• Integrated physics-informed learning and resonance process signature for the prediction of fatigue crack growth for laser-fused alloys
• Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
• A novel solution of fluid equations for radio-frequency plasmas by physics-informed neural networks with transfer learning
• A physics-constrained neural network for multiphase flows
• General transformation neural networks: A class of parametrized functions for high-dimensional function approximation
• Physics-Informed Neural Networks for Solving Multiscale Mode-Resolved Phonon Boltzmann Transport Equation
• Non-intrusive structural-preserving sequential data assimilation
• Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
• Physics-informed Kolmogorov–Arnold networks to model flow in heterogeneous porous media with a mixed pressure-velocity formulation
• Spectral-fPINNs: spectral method based fractional physics-informed neural networks for solving fractional partial differential equations
• Simulation free reliability analysis: A physics-informed deep learning based approach
• MNO: Multiscale Neural Operator for Computational Fluid Dynamics with 3D Point Cloud Data
• Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
• Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systems
• SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws
• 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
• Discovering How Ice Crystals Grow Using Neural ODE's and Symbolic Regression
• A multiscale differential‐algebraic neural network‐based method for learning dynamical systems
• Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
• Trajectory Optimization for Minimum Threat Exposure using Physics-Informed Neural Networks
• Physics-Informed Large Language Models for HVAC Anomaly Detection with Autonomous Rule Generation
• CBINNS: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification
• DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimization
• Efficient High-Accuracy PDEs Solver with the Linear Attention Neural Operator
• Trace Regularity PINNs: Enforcing H^(1)/(2)(∂ Ω) for Boundary Data
• Deep Neural ODE Operator Networks for PDEs
• Finding geodesics with the Deep Ritz method
• Operator Flow Matching for Timeseries Forecasting
• Biology-informed neural networks learn nonlinear representations from omics data to improve genomic prediction and interpretability
• A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes
• A DeepLagrangian method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems
• Functional and parametric identifiability for universal differential equations applied to chemical reaction networks
• ADCME: Learning Spatially-varying Physical Fields using Deep Neural Networks
• Quantum machine learning and quantum-inspired methods applied to computational fluid dynamics: a short review
• Neural Network approximation power on homogeneous and heterogeneous reaction-diffusion equations
• Nonlinear dynamics in breathing-soliton lasers
• Narrow Operator Models of Stellarator Equilibria in Fourier Zernike Basis
• Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs
• APRIL: Auxiliary Physically-Redundant Information in Loss - A physics-informed framework for parameter estimation with a gravitational-wave case study
• Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamics
• Data-driven Soliton Manifold Approximations for Dark and Bright Waves: Some Prototypical 1d Case Examples
• Physics-Informed Neural Network Modeling of Vehicle Collision Dynamics in Precision Immobilization Technique Maneuvers
• Functional tensor train neural network for solving high-dimensional PDEs
• AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
• Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling
• Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs
• Causality-guided adaptive sampling method for physics-informed neural networks solving forward problems of partial differential equations
• A kernel framework for learning differential equations and their solution operators
• Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators
• Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
• Heterogeneous Graph Representation of Stiffened Panels with Non-Uniform Boundary Conditions and Loads
• Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks
• Solving the BGK Model and Boltzmann equation by Fourier Neural Operator with conservative constraints
• PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
• Nonlinear discretizations and Newton's method: characterizing stationary points of regression objectives
• DeepMartingale: Duality of the Optimal Stopping Problem with Expressivity
• Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling
• FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with O(Nlog N) Complexity
• TorchCor: High-Performance Cardiac Electrophysiology Simulations with the Finite Element Method on GPUs
• Rough Path Signatures: Learning Neural RDEs for Portfolio Optimization
• Self-Attention to Operator Learning-based 3D-IC Thermal Simulation
• Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
• Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction
• Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data
• Physics-informed neural networks in water and wastewater systems: a critical review
• Room impulse response reconstruction with physics-informed deep learning
• Accelerating kinetic plasma simulations with machine learning generated initial conditions
• The Anatomy of Coronary Risk: How Arterial Geometry Shapes Coronary Artery Disease Through Blood Flow Haemodynamics – Latest Methods, Insights and Clinical Implications
• PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
• Temporal Lifting as Latent-Space Regularization for Continuous-Time Flow Models in AI Systems
• A Morphology-Adaptive Random Feature Method for Inverse Source Problem of the Helmholtz Equation
• AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
• Residual-Informed Learning of Solutions to Algebraic Loops
• Solving Fokker-Planck-Kolmogorov Equation by Distribution Self-adaptation Normalized Physics-informed Neural Networks
• Spatio-Temporal Graph Convolutional Networks for EV Charging Demand Forecasting Using Real-World Multi-Modal Data Integration
• Quantum Random Feature Method for Solving Partial Differential Equations
• Deep Neural Networks Inspired by Differential Equations
• Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method
• Climate Modeling with Neural Diffusion Equations
• Discovery of Governing Equations with Recursive Deep Neural Networks
• Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
• A Narwhal-Inspired Sensing-to-Control Framework for Small Fixed-Wing Aircraft
• A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
• Accelerating wave simulations with neural dispersion correctors
• Physics-informed neural network for predicting <i>in vacuo</i> vocal fold eigenmodes: A proof of concept study
• Physics-Informed Neural Network Methods for Predicting Plant Height Development
• SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
• Neural Network Surrogates for Free Energy Computation of Complex Chemical Systems
• Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
• AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
• The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
• StruSR: Structure-Aware Symbolic Regression with Physics-Informed Taylor Guidance
• RheOFormer: A generative transformer model for simulation of complex fluids and flows
• VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
• INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
• PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling
• Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks
• Generative Models for Helmholtz Equation Solutions: A Dataset of Acoustic Materials
• Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields
• RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics
• Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
• QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
• Physics-Informed Machine Learning in Biomedical Science and Engineering
• Decoding Partial Differential Equations: Cross-Modal Adaptation of Decoder-only Models to PDEs
• Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
• Physics-informed Attention-enhanced Fourier Neural Operator for Solar Magnetic Field Extrapolations
• Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
• Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
• Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models
• Learning to Predict Chaos: Curriculum-Driven Training for Robust Forecasting of Chaotic Dynamics
• Aneurysm Growth Time Series Reconstruction Using Physics-informed Autoencoder
• A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
• Nyström-Accelerated Primal LS-SVMs: Breaking the O(an³) Complexity Bottleneck for Scalable ODEs Learning
• A Mathematical Explanation of Transformers for Large Language Models and GPTs
• Wave-PDE Nets: Trainable Wave-Equation Layers as an Alternative to Attention
• A Hybrid GNN-IZR Framework for Fast and Empirically Robust AC Power Flow Analysis in Radial Distribution Systems
• Robust and efficient solvers for nonlinear partial differential equations based on random feature method
• A Parametric Level Set Method for Topology Optimization based on Deep Neural Network (DNN)
• FieldFormer: Locality-Aware Transformers for Spatio-Temporal Modeling on Sparse Sensor Networks
• Joint Stochastic Optimal Control and Stopping in Aquaculture: Finite-Difference and PINN-Based Approaches
• A physics-informed neural network approach to the point defect model for electrochemical oxide film growth
• PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation
• A derivative-free localized stochastic method for very high-dimensional semilinear parabolic PDEs
• Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows
• Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
• Physics-Informed Neural Operator for Learning Partial Differential Equations
• On the joint observability of flow fields and particle properties from Lagrangian trajectories: evidence from neural data assimilation
• Randomized Matrix Sketching for Neural Network Training and Gradient Monitoring
• Physics-Informed Neural Controlled Differential Equations for Scalable Long Horizon Multi-Agent Motion Forecasting
• Efficient E(3)-equivariant framework for universal charge density prediction
• Neural Operator: Graph Kernel Network for Partial Differential Equations
• Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems
• Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
• Reservoir computing based predictive reduced order model for steel grade intermixing in an industrial continuous casting tundish
• PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils
• Trustworthy AI in numerics: On verification algorithms for neural network-based PDE solvers
• Machine Learning and Control: Foundations, Advances, and Perspectives
• WAN3DNS: Weak Adversarial Networks for Solving 3D Incompressible Navier-Stokes Equations
• Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs
• Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
• Cross-Model Verification of Wall-Bounded Flows using Finite-JAX
• Multi-patch isogeometric neural solver for partial differential equations on computer-aided design domains
• Aspects of holographic entanglement using physics-informed-neural-networks
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement
• Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
• Coupling Physics Informed Neural Networks with External Solvers
• Learning to Solve Optimization Problems Constrained with Partial Differential Equations
• Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks
• A study of Universal ODE approaches to predicting soil organic carbon
• SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics
• Physics-informed neural networks for programmable origami metamaterials with controlled deployment
• Understanding and mitigating gradient pathologies in physics-informed neural networks
• A Biophysical-Model-Informed Source Separation Framework For EMG Decomposition
• VFSI: Validity First Spatial Intelligence for Constraint-Guided Traffic Diffusion
• Quantifying constraint hierarchies in Bayesian PINNs via per-constraint Hessian decomposition
• AW-EL-PINNs: A Multi-Task Learning Physics-Informed Neural Network for Euler-Lagrange Systems in Optimal Control Problems
• Statistical Learning Guarantees for Group-Invariant Barron Functions
• Modeling Sediment Fluxes From Debris‐Rich Basal Ice Layers
• Deep Learning for Subspace Regression
• Network-based analysis of fluid flows: Progress and outlook
• Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources
• Beyond Heuristics: Globally Optimal Configuration of Implicit Neural Representations
• From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs
• PHASE: Physics-Integrated, Heterogeneity-Aware Surrogates for Scientific Simulations
• Beyond Gaussian Initializations: Signal Preserving Weight Initialization for Odd-Sigmoid Activations
• F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning
• Modeling with uncertainty quantification reveals the essentials of a non-canonical algal carbon-concentrating mechanism
• Deep learning probability flows and entropy production rates in active matter
• Solving PDEs on spheres with physics-informed convolutional neural networks
• Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
• A deep learning framework for solution and discovery in solid mechanics
• Reparameterizing 4DVAR with neural fields
• AI for Sustainable Future Foods
• Model reduction of parametric ordinary differential equations via autoencoders: structure-preserving latent dynamics and convergence analysis
• Data-driven Neural Networks for Windkessel Parameter Calibration
• Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification
• Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders
• MORPH: PDE Foundation Models with Arbitrary Data Modality
• PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
• NewtonGen: Physics-Consistent and Controllable Text-to-Video Generation via Neural Newtonian Dynamics
• Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution
• MDBench: Benchmarking Data-Driven Methods for Model Discovery
• PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation
• Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
• Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
• Physics-Informed Neural Networks for Heat Transfer Problems
• Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
• Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
• Sensor optimization for urban wind estimation with cluster-based probabilistic framework
• PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models
• Data-free neural PDE solvers based on Graph Neural Networks and weak forms
• Machine Learning in Next-Generation Polymer Composites: Recent Advances and Perspectives
• Write-Safe Flow Field Mapping under Ambiguous Onboard Sensing and Localization Drift
• Towards a typology for hybrid compound flood modeling
• Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries
• Routes towards an effective AI in CFD: an epistemological and technical perspective
• Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction
• Numerical Spectrum Linking: Identification of Governing PDE via Koopman-Chebyshev Approximation with Resampling
• Can AI Follow In Einstein's Footsteps?
• Physics-Informed Neural Networks for 2D Plane Wave Scattering in Arbitrary Dielectric Structures
• CLVisc Agent for autonomous relativistic hydrodynamics studies
• Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem
• Physics-informed neural networks (PINNs) for fluid mechanics: A review
• MATLAB Implementation of Physics Informed Deep Neural Networks for Forward and Inverse Structural Vibration Problems
• Gap-free GNSS-R wind field reconstruction: A neural mapping scheme and initial validation
• Neural network approximation in discrete dual norms with adaptive test spaces
• Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries
• Fusing theory-guided machine learning and bio-sensing: considering time in how children learn science from dynamic multimedia
• Physics-aware, probabilistic model order reduction with guaranteed\n stability
• Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations
• Accurately Solving Physical Systems with Graph Learning
• Physics-informed neural networks for offshore tsunami data assimilation
• A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using a Physics-Informed Neural Network
• Neural Ordinary Differential Equations for Data-Driven Reduced Order\n Modeling of Environmental Hydrodynamics
• Physics-Informed Neural Networks with Hard Constraints for Inverse Design
• Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations
• Interpretable physics-informed graph neural networks for flood forecasting
• Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
• Spectrum-adaptive physics-informed neural network for rapid ocean acoustic field prediction
• Spectral-Prior Guided Multistage Physics-Informed Neural Networks for Highly Accurate PDE Solutions
• Eruption Source Parameters in Volcanic Plume Modeling: Advances, Challenges, and Future Directions
• Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
• Biophysics-Enhanced Neural Representations for Patient-Specific Respiratory Motion Modeling
• A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks
• Physics-informed machine learning: case studies for weather and climate modelling
• THINNs: Thermodynamically Informed Neural Networks
• Choose a Transformer: Fourier or Galerkin
• Reconstruction of three-dimensional turbulent flows from sparse and noisy planar measurements: A weight-sharing neural network approach
• Energy minimisation using overlapping tensor-product free-knot B-splines
• A convergence framework for energy minimisation of linear self-adjoint elliptic PDEs in nonlinear approximation spaces
• Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints
• Learning From Simulators: A Theory of Simulation-Grounded Learning
• Learning Neural Antiderivatives
• Particle Identification with MLPs and PINNs Using HADES Data
• Analysis of the Rarefied Flow at Micro-Step using a DeepONet Surrogate Model with a Physics-Guided Zonal Loss Function
• Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
• A Deep-Learning-Driven Optimization-Based Inverse Solver for Accelerating the Marchenko Method
• Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
• A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
• Philosophy-informed Machine Learning
• Learning Reaction-Diffusion Kinetics from Mechanical Information
• Quantum neural ordinary and partial differential equations
• Evidential Physics-Informed Neural Networks for Scientific Discovery
• A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems
• Indoor Airflow Imaging Using Physics-Informed Background-Oriented Schlieren Tomography
• Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems
• A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
• Solving Differential Equation with Quantum-Circuit Enhanced Physics-Informed Neural Networks
• Semi-Discrete in Time Method for Time-Dependent Equations by Random Neural Basis
• A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
• Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
• Floating-Body Hydrodynamic Neural Networks
• Discovery of Unstable Singularities
• A Physics-Informed Neural Network Framework For Partial Differential Equations on 3D Surfaces: Time-Dependent Problems
• SuNeRF-CME: Physics-Informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections
• Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics
• VEGA: Electric Vehicle Navigation Agent via Physics-Informed Neural Operator and Proximal Policy Optimization
• PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models
• A Data-Aware Fourier Neural Operator for Modeling Spatiotemporal Electromagnetic Fields
• A Deep Learning Based Discontinuous Galerkin Method for Hyperbolic Equations with Discontinuous Solutions and Random Uncertainties
• An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems
• Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions
• Fourier Neural Operator for Parametric Partial Differential Equations
• Error analysis for the deep Kolmogorov method
• Quantum Noise Tomography with Physics-Informed Neural Networks
• PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma
• Model reduction methods for nuclear emulators
• 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
• Learning Singularity-Encoded Green's Functions with Application to Iterative Methods
• Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)
• Acceleration of Multi-Scale LTS Magnet Simulations with Neural Network Surrogate Models
• A Variational Physics-Informed Neural Network Framework Using Petrov-Galerkin Method for Solving Singularly Perturbed Boundary Value Problems
• Physics-informed neural network solves minimal surfaces in curved spacetime
• Gluon scattering amplitudes with instantons and minimal surfaces with topology change
• Simulating and Learning Quantum Evolution: A CTQW-ML Framework
• NuGraph2 with Context-Aware Inputs: Physics-Inspired Improvements in Semantic Segmentation
• Interpretable neural network system identification method for two families of second-order systems based on characteristic curves
• Physics-Informed Kolmogorov-Arnold Networks for multi-material elasticity problems in electronic packaging
• Physics-informed neural networks (PINNs) for fluid mechanics: a review
• Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
• Physics-informed sensor coverage through structure preserving machine learning
• Stochastic spectral embedding
• SciML Agents: Write the Solver, Not the Solution
• Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Noise-Stressed Synthetic Conditions
• Variational Neural Networks for Observable Thermodynamics (V-NOTS)
• Uncertainty Propagation Networks for Neural Ordinary Differential Equations
• ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
• Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation
• Learning-Based Data-Assisted Port-Hamiltonian Control for Free-Floating Space Manipulators
• SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video
• Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
• Design-GenNO: A Physics-Informed Generative Model with Neural Operators for Inverse Microstructure Design
• FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
• Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
• Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space Dynamics
• Spectral Bottleneck in Sinusoidal Representation Networks: Noise is All You Need
• Data-driven nonlinear aerodynamics models with certifiably optimal boundedness properties
• Can deep learning beat numerical weather prediction?
• Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
• Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
• Physics-informed low-rank neural operators with application to parametric elliptic PDEs
• Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion
• An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems
• IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
• THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks
• Data-driven discovery of dynamical models in biology
• Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning
• Learning spatially structured open quantum dynamics with regional-attention transformers
• Text-Trained LLMs Can Zero-Shot Extrapolate PDE Dynamics, Revealing a Three-Stage In-Context Learning Mechanism
• TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion
• Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks
• A Physics-Informed Neural Networks-Based Model Predictive Control Framework for SIR Epidemics
• Vector-based loss functions for turbulent flow field inpainting
• Accelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models
• HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions
• Measuring unsteady drag of the flow around a sphere based on time series displacement measurements using physics-informed neural networks
• Neuro-Spectral Architectures for Causal Physics-Informed Networks
• Physics-Informed Neural Networks for Nonlocal Beam Eigenvalue Problems
• Error analysis for learning the time-stepping operator of evolutionary PDEs
• Moiré spintronics: Emergent phenomena, material realization and machine learning accelerating discovery
• Wavefield solutions from machine learned functions
• Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification
• Accurate and scalable deep Maxwell solvers using multilevel iterative methods
• Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
• InWaveSR: Topography-Aware Super-Resolution Network for Internal Solitary Waves
• Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models
• Disentangling Target Lines from Interlopers and Continuum with Neural Networks: A SPHEREx Intensity Mapping Case Study
• NVIDIA SimNet^TM: an AI-accelerated multi-physics simulation framework
• A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs
• PIANO: Physics Informed Autoregressive Network
• Exploring accuracy and uncertainty quantification in physics-informed neural networks for inferring microbial community dynamics
• Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems
• INF-3DP: Implicit Neural Fields for Collision-Free Multi-Axis 3D Printing
• CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions
• Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport
• A novel auxiliary equation neural networks method for exactly explicit solutions of nonlinear partial differential equations
• Physics-Guided Neural Networks for Constructing Nucleon-Nucleon Inverse Potentials
• Global convergence of adaptive least-squares finite element methods for nonlinear PDEs
• Neural-Network Chemical Emulator for First-Star Formation: Robust Iterative Predictions over a Wide Density Range
• The Need for Verification in AI-Driven Scientific Discovery
• An affinity based opinion dynamics model for the evolving pattern of political polarization
• RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
• Model Predictive Control for a Soft Robotic Finger with Stochastic Behavior based on Fokker-Planck Equation
• Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
• Data-driven modeling for flow reconstruction from sparse temperature measurements
• HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks
• MD-PNOP: Equation-Recast Neural Operators for Minimal-Data Extrapolation and PDE Solver Acceleration
• TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks
• A Hybrid Discontinuous Galerkin Neural Network Method for Solving Hyperbolic Conservation Laws with Temporal Progressive Learning
• Predicting Brain Morphogenesis via Physics-Transfer Learning
• Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts
• Machine Learning of Partial Differential Equations from Noise Data
• An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network
• Learning with Mandelbrot and Julia
• Deep Tangent Bundle (DTB) method: a Deep Neural Network approach to compute solutions of PDES
• Self-supervised neural operator for solving partial differential equations
• Deep neural networks for geometric multigrid methods
• Deep Learning for Personalized Binaural Audio Reproduction
• Continuously Tempered Diffusion Samplers
• Theory Foundation of Physics-Enhanced Residual Learning
• Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
• FNODE: Flow-Matching for data-driven simulation of constrained multibody systems
• Generative Latent Space Dynamics of Electron Density
• Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
• Multiwavelet-based Operator Learning for Differential Equations
• Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks for the Poisson Equation
• Coherent motions to predict Lagrangian trajectories
• Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
• Data-Driven Bifurcation Handling in Physics-Based Reduced-Order Vascular Hemodynamic Models
• Fast Convergence Rates for Subsampled Natural Gradient Algorithms on Quadratic Model Problems
• Physics-Constrained Machine Learning for Chemical Engineering
• D3PINNs: A Novel Physics-Informed Neural Network Framework for Staged Solving of Time-Dependent Partial Differential Equations
• Artificial neural network solver for Fokker-Planck and Koopman eigenfunctions
• Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
• Neural Field Turing Machine: A Differentiable Spatial Computer
• Objective Value Change and Shape-Based Accelerated Optimization for the Neural Network Approximation
• Energy-Equidistributed Moving Sampling Physics-informed Neural Networks for Solving Conservative Partial Differential Equations
• Inductive Domain Transfer In Misspecified Simulation-Based Inference
• Neural Spline Operators for Risk Quantification in Stochastic Systems
• Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
• Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management
• A deep first-order system least squares method for the obstacle problem
• Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks
• Learning Robust Regions of Attraction Using Rollout-Enhanced Physics-Informed Neural Networks with Policy Iteration
• DETNO: A Diffusion-Enhanced Transformer Neural Operator for Long-Term Traffic Forecasting
• Neural operators for solving nonlinear inverse problems
• STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
• Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
• Deep learning in automated ultrasonic NDE – Developments, axioms and opportunities
• Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction
• The Sound of Risk: A Multimodal Physics-Informed Acoustic Model for Forecasting Market Volatility and Enhancing Market Interpretability
• Improving Long-term Autoregressive Spatiotemporal Predictions: A Proof of Concept with Fluid Dynamics
• Numerical Analysis of Unsupervised Learning Approaches for Parameter Identification in PDEs
• From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology
• PKG-DPO: Optimizing Domain-Specific AI systems with Physics Knowledge Graphs and Direct Preference Optimization
• Eig-PIELM: A Mesh-Free Approach for Efficient Eigen-Analysis with Physics-Informed Extreme Learning Machines
• Training Transformers for Mesh-Based Simulations
• Deep learning for the semi-classical limit of the Schrödinger equation
• Hybrid Least Squares/Gradient Descent Methods for DeepONets
• Neural Robot Dynamics
• Exploring spatiotemporal patterns in the Kuralay-II system using a neural network
• Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening
• VarNet: Variational Neural Networks for the Solution of Partial Differential Equations
• Convolutional-network models to predict wall-bounded turbulence from wall quantities
• ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
• Learning-based Traffic State Reconstruction using Probe Vehicles
• CALYPSO: Forecasting and Analyzing MRSA Infection Patterns with Community and Healthcare Transmission Dynamics
• Uncertainty Tube Visualization of Particle Trajectories
• Machine learning in fluid dynamics: A critical assessment
• Human Digital Twin: Data, Models, Applications, and Challenges
• Multiscale DeepONet for Nonlinear Operators in Oscillatory Function Spaces for Building Seismic Wave Responses
• Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
• Physics-informed deep operator network for traffic state estimation
• Generalization vs. Memorization in Autoregressive Deep Learning: Or, Examining Temporal Decay of Gradient Coherence
• A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators
• Uncovering Emergent Physics Representations Learned In-Context by Large Language Models
• Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems
• Strategies for training point distributions in physics-informed neural networks
• The Mathematical Theory of Behavioural Swarms: Towards Modelling the Collective Dynamics of Living Systems
• Deep Coregionalization for the Emulation of Spatial-Temporal Fields
• Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
• Higher-order Quasi-Monte Carlo Training of Deep Neural Networks
• A Latent space solver for PDE generalization
• Cosmology-informed Neural Networks to infer dark energy equation-of-state
• Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
• Polynomial-Spline Neural Networks with Exact Integrals
• Meta-learning Structure-Preserving Dynamics
• Goal-Oriented Low-Rank Tensor Decompositions for Numerical Simulation Data
• Physics-Informed Reward Machines
• Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
• Nonlinear filtering based on density approximation and deep BSDE prediction
• Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
• Sum-of-Gaussians tensor neural networks for high-dimensional Schrödinger equation
• SSBE-PINN: A Sobolev Boundary Scheme Boosting Stability and Accuracy in Elliptic/Parabolic PDE Learning
• Estimating carbon pools in the shelf sea environment: reanalysis or model-informed machine learning?
• TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos
• Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules
• DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems
• Parameter-Aware Ensemble SINDy for Interpretable Symbolic SGS Closure
• An artificial neural network approximation for Cauchy inverse problems
• LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
• Evolution of linear matter perturbations with error-bounded bundle physics-informed neural networks
• Learning an Implicit Physics Model for Image-based Fluid Simulation
• Prediction error certification for PINNs: Theory, computation, and application to Stokes flow
• Learning Robust Satellite Attitude Dynamics with Physics-Informed Normalising Flow
• Forecasting Continuous Non-Conservative Dynamical Systems in SO(3)
• Barron Space Representations for Elliptic PDEs with Homogeneous Boundary Conditions
• Physics-informed Multiresolution Wavelet Neural Network Method for Solving Partial Differential Equations
• Fast and Generalizable parameter-embedded Neural Operators for Lithium-Ion Battery Simulation
• Unsupervised operator learning approach for dissipative equations via Onsager principle
• Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
• An RBC-MsUQ Framework for Red Blood Cell Morpho-Mechanics
• Efficient data-driven regression for reduced-order modeling of spatial pattern formation
• Diffeomorphic Neural Operator Learning
• Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology
• Neural Networks as Surrogate Solvers for Time-dependent Accretion Disk Dynamics
• Conservative data-driven finite element framework with adaptive hp-refinement for diffusion problems with material uncertainty
• Physics-Enforced Modeling for Insertion Loss of Transmission Lines by Deep Neural Networks
• FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
• Fast, Convex and Conditioned Network for Multi-Fidelity Vectors and Stiff Univariate Differential Equations
• Uncovering the Underlying Physics of Degrading System Behavior Through a Deep Neural Network Framework: The Case of Remaining Useful Life Prognosis
• Optimal Linear Baseline Models for Scientific Machine Learning
• Learning Geometric-Aware Quadrature Rules for Functional Minimization
• Deep Neural Networks with General Activations: Super-Convergence in Sobolev Norms
• SO-PIFRNN: Self-optimization physics-informed Fourier-features randomized neural network for solving partial differential equations
• The use of physics-informed neural network approach to image restoration via nonlinear PDE tools
• Physics-Informed Neural Network for Elastic Wave-Mode Separation
• Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
• GFocal: A Global-Focal Neural Operator for Solving PDEs on Arbitrary Geometries
• Case Studies of Generative Machine Learning Models for Dynamical Systems
• Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions
• Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
• Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network
• Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology
• Bridging ocean wave physics and deep learning: Physics-informed neural operators for nonlinear wavefield reconstruction in real-time
• Physics-informed Neural Networks for Elliptic Partial Differential Equations on 3D Manifolds
• Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
• Quantum Spectral Reasoning: A Non-Neural Architecture for Interpretable Machine Learning
• Physics-informed Neural Time Fields for Prehensile Object Manipulation
• BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics
• Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs
• Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots
• Epi²-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networks
• Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach
• The Vanishing Gradient Problem for Stiff Neural Differential Equations
• Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting
• Partial observations and conservation laws: Grey-box modeling in\n biotechnology and optogenetics
• A Reward-Directed Diffusion Framework for Generative Design Optimization
• Fine-tuning physics-informed neural networks for cavity flows using coordinate transformation
• MASIV: Toward Material-Agnostic System Identification from Videos
• Separated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs
• Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variables
• DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios
• End-to-end differentiable learning of turbulence models from indirect observations
• Search for t tt tW Production at √(s) = 13 TeV Using a Modified Graph Neural Network at the LHC
• Quantifying and Visualizing Sim-to-Real Gaps: Physics-Guided Regularization for Reproducibility
• Physics-guided denoiser network for enhanced additive manufacturing data quality
• Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models
• AI paradigm for solving differential equations: first-principles data generation and scale-dilation operator AI solver
• Stability-Constrained AC Optimal Power Flow--A Gaussian Process-Based Approach
• Data Readiness for Scientific AI at Scale
• A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains
• Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks
• Physics-Informed EvolveGCN: Satellite Prediction for Multi Agent Systems
• Neural network extraction of chromo-electric and chromo-magnetic gluon masses
• Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control
• Physics-Informed Neural Networks with Dynamical Boundary Constraints
• Improving Neural Network Training using Dynamic Learning Rate Schedule for PINNs and Image Classification
• PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
• Stochastic forest transition model dynamics and parameter estimation via deep learning
• 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
• Deep Polynomial Chaos Expansion
• Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy
• Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights