VW-PINNs: A volume weighting method for PDE residuals in physics-informed neural networks

Acta Mechanica Sinica, Vol. 41 (2025), Iss. 3

Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed Neural Networks

The European Physical Journal E, Vol. 46 (2023), Iss. 3

Physics-informed neural network for engineers: a review from an implementation aspect

Journal of Mechanical Science and Technology, Vol. 38 (2024), Iss. 7 P.3499

A Taxonomic Survey of Physics-Informed Machine Learning

Applied Sciences, Vol. 13 (2023), Iss. 12 P.6892

Deep Ritz Method for Elliptical Multiple Eigenvalue Problems

Journal of Scientific Computing, Vol. 98 (2024), Iss. 2

Schwarz Waveform Relaxation Physics Informed Neural Networks for Solving Advection-Diffusion-Reaction Equations

SSRN Electronic Journal , Vol. (2021), Iss.

DeepFEM: A Novel Element-Based Deep Learning Approach for Solving Nonlinear Partial Differential Equations in Computational Solid Mechanics

Journal of Engineering Mechanics, Vol. 149 (2023), Iss. 2

A Physics-Informed Neural Network-Based Waveguide Eigenanalysis

IEEE Access, Vol. 12 (2024), Iss. P.120777

A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems

Engineering Applications of Artificial Intelligence, Vol. 127 (2024), Iss. P.107256

A neural network approach for the reliability analysis on failure of shallow foundations on cohesive soils

International Journal of Geo-Engineering, Vol. 15 (2024), Iss. 1

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Computer Methods in Applied Mechanics and Engineering, Vol. 403 (2023), Iss. P.115671

Knowledge Science, Engineering and Management

udPINNs: An Enhanced PDE Solving Algorithm Incorporating Domain of Dependence Knowledge

2023

Physics-informed machine learning model for computational fracture of quasi-brittle materials without labelled data

International Journal of Mechanical Sciences, Vol. 223 (2022), Iss. P.107282

Time-dependent Dirac Equation with Physics-Informed Neural Networks: Computation and Properties

Computer Physics Communications, Vol. 280 (2022), Iss. P.108474

Parametric extended physics-informed neural networks for solid mechanics with complex mixed boundary conditions

Journal of the Mechanics and Physics of Solids, Vol. 194 (2025), Iss. P.105944

TCAS-PINN: Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Chinese Physics B, Vol. 33 (2024), Iss. 5 P.050701

A data-driven physics-constrained deep learning computational framework for solving von Mises plasticity

Engineering Applications of Artificial Intelligence, Vol. 122 (2023), Iss. P.106049

Physics informed neural network consisting of two decoupled stages

Guler Bayazit, Nilgun

Engineering Science and Technology, an International Journal, Vol. 46 (2023), Iss. P.101489

A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using a Physics-Informed Neural Network

SIAM Journal on Scientific Computing, Vol. 46 (2024), Iss. 4 P.C479

Unified finite-volume physics informed neural networks to solve the heterogeneous partial differential equations

Knowledge-Based Systems, Vol. 295 (2024), Iss. P.111831

Neuro‐PINN: A hybrid framework for efficient nonlinear projection equation solutions

International Journal for Numerical Methods in Engineering, Vol. 125 (2024), Iss. 5

A practical PINN framework for multi-scale problems with multi-magnitude loss terms

Journal of Computational Physics, Vol. 510 (2024), Iss. P.113112

Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis

Big Data and Cognitive Computing, Vol. 6 (2022), Iss. 4 P.140

Application of improved physics-informed neural networks for nonlinear consolidation problems with continuous drainage boundary conditions

Acta Geotechnica, Vol. 19 (2024), Iss. 1 P.495

Physics-informed neural networks for periodic flows

Physics of Fluids, Vol. 36 (2024), Iss. 7

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

Engineering Applications of Artificial Intelligence, Vol. 126 (2023), Iss. P.107183

Boussinesq equation solved by the physics-informed neural networks

Nonlinear Dynamics, Vol. 111 (2023), Iss. 16 P.15279

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions

Journal of Computational Physics, Vol. 493 (2023), Iss. P.112464

Physics-informed neural network based topology optimization through continuous adjoint

Structural and Multidisciplinary Optimization, Vol. 67 (2024), Iss. 8

Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics

Neural Networks, Vol. 180 (2024), Iss. P.106703

Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

International Journal of Electrical Power & Energy Systems, Vol. 157 (2024), Iss. P.109741

Physics-Informed Neural Networks (PINNs) for gravity field modelling and representation: With application to asteroid EROS

International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications, Shenzhen, China, May 19–22, 2024, (2024), P.302

Robot Intelligence Technology and Applications 7

Estimation of Aerodynamic Uncertainty in Missile System Using Physics-Informed Neural Network Framework

2023

A framework based on symbolic regression coupled with eXtended Physics-Informed Neural Networks for gray-box learning of equations of motion from data

Computer Methods in Applied Mechanics and Engineering, Vol. 415 (2023), Iss. P.116258

Data-driven soliton solution implementation based on nonlinear adaptive physics-informed neural networks

Nonlinear Dynamics, Vol. (2024), Iss.

Big Data and Security

Asymptotic Physics-Informed Neural Networks for Solving Singularly Perturbed Problems

2024

Fractional physics-informed neural networks for time-fractional phase field models

Nonlinear Dynamics, Vol. 110 (2022), Iss. 3 P.2715

Optimization of Physics-Informed Neural Networks for Solving the Nolinear Schrödinger Equation

Doklady Mathematics, Vol. 108 (2023), Iss. S2 P.S186

Physics-driven proper orthogonal decomposition: A simulation methodology for partial differential equations

MethodsX, Vol. 10 (2023), Iss. P.102204

Proceedings of the Vortex Workshop

Spatio-Temporal Flow Field Prediction of Turbulent Boundary Layer Based on PINN

2024

Spectrally adapted physics-informed neural networks for solving unbounded domain problems

Machine Learning: Science and Technology, Vol. 4 (2023), Iss. 2 P.025024

Pre-Training Physics-Informed Neural Network with Mixed Sampling and Its Application in High-Dimensional Systems

Journal of Systems Science and Complexity, Vol. 37 (2024), Iss. 2 P.494

On the spectral bias of coupled frequency predictor–corrector triangular DNN: The convergence analysis

Zhang, Rui

Japan Journal of Industrial and Applied Mathematics, Vol. 41 (2024), Iss. 1 P.545

A Computational Framework for the Indirect Estimation of Interface Thermal Resistance of Composite Materials Using Xpinns

SSRN Electronic Journal , Vol. (2022), Iss.

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

Nature Computational Science, Vol. 1 (2021), Iss. 12 P.819

Three ways to solve partial differential equations with neural networks — A review

GAMM-Mitteilungen, Vol. 44 (2021), Iss. 2

A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

Journal of Computational Physics, Vol. 462 (2022), Iss. P.111260

Physics-Informed Neural Network (PINN) model for predicting subgrade settlement induced by shield tunnelling beneath an existing railway subgrade

Transportation Geotechnics, Vol. 49 (2024), Iss. P.101409

Physics-informed neural network combined with characteristic-based split for solving forward and inverse problems involving Navier–Stokes equations

Neurocomputing, Vol. 573 (2024), Iss. P.127240

An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator

Computer Methods in Applied Mechanics and Engineering, Vol. 407 (2023), Iss. P.115944

A comparative investigation of a time‐dependent mesh method and physics‐informed neural networks to analyze the generalized Kolmogorov–Petrovsky–Piskunov equation

International Journal for Numerical Methods in Fluids, Vol. 96 (2024), Iss. 5 P.651

General-Kindred physics-informed neural network to the solutions of singularly perturbed differential equations

Physics of Fluids, Vol. 36 (2024), Iss. 11

Physics-Informed Neural Networks for Solving Dynamic Two-Phase Interface Problems

SIAM Journal on Scientific Computing, Vol. 45 (2023), Iss. 6 P.A2912

Thermal conductivity estimation using Physics-Informed Neural Networks with limited data

Engineering Applications of Artificial Intelligence, Vol. 137 (2024), Iss. P.109079

h-Analysis and data-parallel physics-informed neural networks

Scientific Reports, Vol. 13 (2023), Iss. 1

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

Scientific Reports, Vol. 13 (2023), Iss. 1

Physics-informed neural networks (PINNs) for fluid mechanics: a review

Acta Mechanica Sinica, Vol. 37 (2021), Iss. 12 P.1727

Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations

International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, Vol. 37 (2024), Iss. 4

A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs

Journal of Computational Physics, Vol. 477 (2023), Iss. P.111912

CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries

Computer Methods in Applied Mechanics and Engineering, Vol. 400 (2022), Iss. P.115491

A Neural Network-Based Poisson Solver for Fluid Simulation

Neural Processing Letters, Vol. 56 (2024), Iss. 5

Physics informed neural networks for simulating radiative transfer

Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 270 (2021), Iss. P.107705

Assimilation of statistical data into turbulent flows using physics-informed neural networks

The European Physical Journal E, Vol. 46 (2023), Iss. 3

High Performance Computing

Improving Boundary Layer Predictions Using Parametric Physics-Aware Neural Networks

2022

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Engineering Research Express, Vol. 5 (2023), Iss. 3 P.032003

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Physics of Fluids, Vol. 36 (2024), Iss. 10

Machine learning for nonlinear integro-differential equations with degenerate kernel scheme

Communications in Nonlinear Science and Numerical Simulation, Vol. 138 (2024), Iss. P.108242

Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 3

Model Updating for Structural Digital Twins Through Physics-Informed Data-Driven Models

2024

Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations

Engineering Applications of Artificial Intelligence, Vol. 133 (2024), Iss. P.108085

Physics-Informed neural network solver for numerical analysis in geoengineering

Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, Vol. 18 (2024), Iss. 1 P.33

Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks

Heliyon, Vol. 9 (2023), Iss. 8 P.e18820

A Finite Element based Deep Learning solver for parametric PDEs

Computer Methods in Applied Mechanics and Engineering, Vol. 391 (2022), Iss. P.114562

Physics-informed neural networks for mesh deformation with exact boundary enforcement

Engineering Applications of Artificial Intelligence, Vol. 125 (2023), Iss. P.106660

Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks

Engineering Applications of Artificial Intelligence, Vol. 138 (2024), Iss. P.109378

Physics-Informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media

Mathematics, Vol. 12 (2023), Iss. 1 P.63

Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: Error analysis and algorithms

Journal of Computational Physics, Vol. 495 (2023), Iss. P.112527

Generalized conditional symmetry enhanced physics-informed neural network and application to the forward and inverse problems of nonlinear diffusion equations

Chaos, Solitons & Fractals, Vol. 168 (2023), Iss. P.113169

Adaptive trajectories sampling for solving PDEs with deep learning methods

Applied Mathematics and Computation, Vol. 481 (2024), Iss. P.128928

The need for adoption of neural HPC (NeuHPC) in space sciences

Frontiers in Astronomy and Space Sciences, Vol. 10 (2023), Iss.

A physics-informed neural network based on mixed data sampling for solving modified diffusion equations

Scientific Reports, Vol. 13 (2023), Iss. 1

Theory-inspired machine learning—towards a synergy between knowledge and data

Welding in the World, Vol. 66 (2022), Iss. 7 P.1291

Real-time full-field inference of displacement and stress from sparse local measurements using physics-informed neural networks

Mechanical Systems and Signal Processing, Vol. 224 (2025), Iss. P.112009

Distributed PINN for Linear Elasticity — A Unified Approach for Smooth, Singular, Compressible and Incompressible Media

International Journal of Computational Methods, Vol. 19 (2022), Iss. 08

A multifidelity approach to continual learning for physical systems

Machine Learning: Science and Technology, Vol. 5 (2024), Iss. 2 P.025042

wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws

SIAM Journal on Numerical Analysis, Vol. 62 (2024), Iss. 2 P.811

Incorporating sufficient physical information into artificial neural networks: A guaranteed improvement via physics-based Rao-Blackwellization

Computer Methods in Applied Mechanics and Engineering, Vol. 423 (2024), Iss. P.116848

Determining COVID-19 Dynamics Using Physics Informed Neural Networks

Axioms, Vol. 11 (2022), Iss. 3 P.121

Physics-informed neural network for diffusive wave model

Journal of Hydrology, Vol. 637 (2024), Iss. P.131261

Numerical Analysis Meets Machine Learning

Theoretical foundations of physics-informed neural networks and deep neural operators

2024

A deep learning approach based on the physics-informed neural networks for Gaussian thermal shock-induced thermoelastic wave propagation analysis in a thick hollow cylinder with energy dissipation

Waves in Random and Complex Media, Vol. (2022), Iss. P.1

Physical informed memory networks based on domain decomposition for solving nonlinear partial differential equations

The European Physical Journal Special Topics, Vol. (2024), Iss.

Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Computer Methods in Applied Mechanics and Engineering, Vol. 429 (2024), Iss. P.117135

Physics-informed neural networks for the shallow-water equations on the sphere

Journal of Computational Physics, Vol. 456 (2022), Iss. P.111024

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Advances in Water Resources, Vol. 160 (2022), Iss. P.104098

Physics-specialized neural network with hard constraints for solving multi-material diffusion problems

Computer Methods in Applied Mechanics and Engineering, Vol. 430 (2024), Iss. P.117223

Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems

Journal of Computational Physics, Vol. 500 (2024), Iss. P.112761

Extremization to fine tune physics informed neural networks for solving boundary value problems

Communications in Nonlinear Science and Numerical Simulation, Vol. 137 (2024), Iss. P.108129

Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks

International Journal of Heat and Fluid Flow, Vol. 96 (2022), Iss. P.109002

A new modified deep learning technique based on physics-informed neural networks (PINNs) for the shock-induced coupled thermoelasticity analysis in a porous material

Journal of Thermal Stresses, Vol. 47 (2024), Iss. 6 P.798

Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations

Ocean Engineering, Vol. 248 (2022), Iss. P.110775

A Neural Network Model for Estimation of Failure Stresses and Strains in Cohesive Soils

Geotechnics, Vol. 2 (2022), Iss. 4 P.1084

Physics-informed neural networks for parametric compressible Euler equations

Computers & Fluids, Vol. 270 (2024), Iss. P.106164

Wavelets based physics informed neural networks to solve non-linear differential equations

Scientific Reports, Vol. 13 (2023), Iss. 1

Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network

Journal of Computational Physics, Vol. 453 (2022), Iss. P.110928

A PINN-based level-set formulation for reconstruction of bubble dynamics

Archive of Applied Mechanics, Vol. 94 (2024), Iss. 9 P.2667

Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data

Green Chemical Engineering, Vol. (2024), Iss.

Dynamic Weight Strategy of Physics-Informed Neural Networks for the 2D Navier–Stokes Equations

Entropy, Vol. 24 (2022), Iss. 9 P.1254

Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning

SIAM Journal on Scientific Computing, Vol. 45 (2023), Iss. 3 P.A1271

Predicting micro-bubble dynamics with semi-physics-informed deep learning

AIP Advances, Vol. 12 (2022), Iss. 3

Physics-informed neural networks with domain decomposition for the incompressible Navier–Stokes equations

Physics of Fluids, Vol. 36 (2024), Iss. 2

Δ-PINNs: Physics-informed neural networks on complex geometries

Engineering Applications of Artificial Intelligence, Vol. 127 (2024), Iss. P.107324

Physics-Informed Neural Networks for Inverse Problems in Supersonic Flows

SSRN Electronic Journal , Vol. (2022), Iss.

HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations

Computers & Mathematics with Applications, Vol. 121 (2022), Iss. P.62

The novel PINN/gPINN-based deep learning schemes for non-Fickian coupled diffusion-elastic wave propagation analysis

Waves in Random and Complex Media, Vol. (2023), Iss. P.1

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

Computer Methods in Applied Mechanics and Engineering, Vol. 379 (2021), Iss. P.113741

A method for computing inverse parametric PDE problems with random-weight neural networks

Journal of Computational Physics, Vol. 489 (2023), Iss. P.112263

Training physics-informed neural networks: One learning to rule them all?

Results in Engineering, Vol. 18 (2023), Iss. P.101023

Physics-Informed Neural Network Solution of Thermo–Hydro–Mechanical Processes in Porous Media

Journal of Engineering Mechanics, Vol. 148 (2022), Iss. 11

Multi-domain physics-informed neural network for solving heat conduction and conjugate natural convection with discontinuity of temperature gradient on interface

Science China Technological Sciences, Vol. 65 (2022), Iss. 10 P.2442

Modeling two-phase flows with complicated interface evolution using parallel physics-informed neural networks

Physics of Fluids, Vol. 36 (2024), Iss. 9

Physical laws meet machine intelligence: current developments and future directions

Artificial Intelligence Review, Vol. 56 (2023), Iss. 7 P.6947

AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis

Thin-Walled Structures, Vol. 196 (2024), Iss. P.111423

Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs

Advances in Computational Mathematics, Vol. 48 (2022), Iss. 6

Learning high frequency data via the coupled frequency predictor-corrector triangular DNN

Zhang, Rui

Japan Journal of Industrial and Applied Mathematics, Vol. 40 (2023), Iss. 2 P.1259

Higher-order multi-scale deep Ritz method (HOMS-DRM) and its convergence analysis for solving thermal transfer problems of composite materials

Computational Mechanics, Vol. (2024), Iss.

Spatial domain decomposition-based physics-informed neural networks for practical acoustic propagation estimation under ocean dynamics

The Journal of the Acoustical Society of America, Vol. 155 (2024), Iss. 5 P.3306

ADLGM: An efficient adaptive sampling deep learning Galerkin method

Journal of Computational Physics, Vol. 477 (2023), Iss. P.111944

Neural networks informed by physics for modeling mass flow rate in a production wellbore

Engineering Applications of Artificial Intelligence, Vol. 128 (2024), Iss. P.107528

Phase space approach to solving higher order differential equations with artificial neural networks

Physical Review Research, Vol. 4 (2022), Iss. 4

Physics-informed neural network-based surrogate model for a virtual thermal sensor with real-time simulation

International Journal of Heat and Mass Transfer, Vol. 214 (2023), Iss. P.124392

Numerical Control: Part B

Nonoverlapping domain decomposition and virtual controls for optimal control problems of p-type on metric graphs

Leugering, Günter

2023

Physics-informed data based neural networks for two-dimensional turbulence

Physics of Fluids, Vol. 34 (2022), Iss. 5

Octree-based hierarchical sampling optimization for the volumetric super-resolution of scientific data

Journal of Computational Physics, Vol. 502 (2024), Iss. P.112804

Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations

Geoscientific Model Development, Vol. 16 (2023), Iss. 24 P.7375

Learning thermoacoustic interactions in combustors using a physics-informed neural network

Engineering Applications of Artificial Intelligence, Vol. 138 (2024), Iss. P.109388

Neural Fields in Visual Computing and Beyond

Computer Graphics Forum, Vol. 41 (2022), Iss. 2 P.641

Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines

Scientific Reports, Vol. 13 (2023), Iss. 1

Neural network-based model predictive control for thin-film chemical deposition of quantum dots using data from a multiscale simulation

Chemical Engineering Research and Design, Vol. 183 (2022), Iss. P.595

Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains

Computer Methods in Applied Mechanics and Engineering, Vol. 389 (2022), Iss. P.114424

Physics-informed convolutional transformer for predicting volatility surface

Quantitative Finance, Vol. 24 (2024), Iss. 2 P.203

Thermodynamically consistent physics-informed neural networks for hyperbolic systems

Journal of Computational Physics, Vol. 449 (2022), Iss. P.110754

Sensitivity analysis using Physics-informed neural networks

Engineering Applications of Artificial Intelligence, Vol. 135 (2024), Iss. P.108764

Kolmogorov n–width and Lagrangian physics-informed neural networks: A causality-conforming manifold for convection-dominated PDEs

Computer Methods in Applied Mechanics and Engineering, Vol. 404 (2023), Iss. P.115810

Pre-training strategy for solving evolution equations based on physics-informed neural networks

Journal of Computational Physics, Vol. 489 (2023), Iss. P.112258

On computing the hyperparameter of extreme learning machines: Algorithm and application to computational PDEs, and comparison with classical and high-order finite elements

Journal of Computational Physics, Vol. 463 (2022), Iss. P.111290

Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains

International Journal for Numerical Methods in Engineering, Vol. 125 (2024), Iss. 4

Learning domain-independent Green’s function for elliptic partial differential equations

Computer Methods in Applied Mechanics and Engineering, Vol. 421 (2024), Iss. P.116779

A novel discretized physics-informed neural network model applied to the Navier–Stokes equations

Physica Scripta, Vol. 99 (2024), Iss. 7 P.076016

Solving parametric elliptic interface problems via interfaced operator network

Journal of Computational Physics, Vol. 514 (2024), Iss. P.113217

Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance

Journal of the Mechanics and Physics of Solids, Vol. 172 (2023), Iss. P.105177

A deep learning method for multi-material diffusion problems based on physics-informed neural networks

Computer Methods in Applied Mechanics and Engineering, Vol. 417 (2023), Iss. P.116395

Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions

Advances in Water Resources, Vol. 185 (2024), Iss. P.104639

Can physics-informed neural networks beat the finite element method?

IMA Journal of Applied Mathematics, Vol. 89 (2024), Iss. 1 P.143

Adaptive transfer learning for PINN

Journal of Computational Physics, Vol. 490 (2023), Iss. P.112291

Research on simulation of gun muzzle flow field empowered by artificial intelligence

Defence Technology, Vol. 32 (2024), Iss. P.196

Physics-constrained neural networks for half-space seismic wave modeling

Computers & Geosciences, Vol. 181 (2023), Iss. P.105477

Multistep asymptotic pre-training strategy based on PINNs for solving steep boundary singular perturbation problems

Computer Methods in Applied Mechanics and Engineering, Vol. 431 (2024), Iss. P.117222

Multi-fidelity meta modeling using composite neural network with online adaptive basis technique

Computer Methods in Applied Mechanics and Engineering, Vol. 388 (2022), Iss. P.114258

Fatigue life prediction driven by mesoscopic defect data

Engineering Applications of Artificial Intelligence, Vol. 131 (2024), Iss. P.107773

On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures

Acta Mechanica, Vol. 234 (2023), Iss. 11 P.5673

On the Hard Boundary Constraint Method for Fluid Flow Prediction based on the Physics-Informed Neural Network

Applied Sciences, Vol. 14 (2024), Iss. 2 P.859

Respecting causality for training physics-informed neural networks

Computer Methods in Applied Mechanics and Engineering, Vol. 421 (2024), Iss. P.116813

Finite element interpolated neural networks for solving forward and inverse problems

Computer Methods in Applied Mechanics and Engineering, Vol. 418 (2024), Iss. P.116505

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Computer Methods in Applied Mechanics and Engineering, Vol. 393 (2022), Iss. P.114823

Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach

SSRN Electronic Journal , Vol. (2021), Iss.

Physics-informed machine learning method for modelling transport of a conservative pollutant in surface water systems

Journal of Hydrology, Vol. 619 (2023), Iss. P.129354

Schwarz waveform relaxation-learning for advection-diffusion-reaction equations

Journal of Computational Physics, Vol. 473 (2023), Iss. P.111657

Sensing prior constraints in deep neural networks for solving exploration geophysical problems

Proceedings of the National Academy of Sciences, Vol. 120 (2023), Iss. 23

Tackling the curse of dimensionality with physics-informed neural networks

Neural Networks, Vol. 176 (2024), Iss. P.106369

Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

IMA Journal of Numerical Analysis, Vol. 42 (2022), Iss. 2 P.981

Ground-penetrating radar wavefield simulation via physics-informed neural network solver

GEOPHYSICS, Vol. 88 (2023), Iss. 2 P.KS47

Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

Computer Methods in Applied Mechanics and Engineering, Vol. 397 (2022), Iss. P.115141

Deep Interface Alternation Method (DIAM) based on domain decomposition for solving elliptic interface problems

Engineering Analysis with Boundary Elements, Vol. 168 (2024), Iss. P.105905

Discovering physics-informed neural networks model for solving partial differential equations through evolutionary computation

Swarm and Evolutionary Computation, Vol. 88 (2024), Iss. P.101589

INN: Interfaced neural networks as an accessible meshless approach for solving interface PDE problems

Journal of Computational Physics, Vol. 470 (2022), Iss. P.111588

Low-temperature plasma simulation based on physics-informed neural networks: Frameworks and preliminary applications

Physics of Fluids, Vol. 34 (2022), Iss. 8

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

Computer Methods in Applied Mechanics and Engineering, Vol. 393 (2022), Iss. P.114778

On the generalization of PINNs outside the training domain and the hyperparameters influencing it

Neural Computing and Applications, Vol. (2024), Iss.

A Preliminary Study on the Resolution of Electro-Thermal Multi-Physics Coupling Problem Using Physics-Informed Neural Network (PINN)

Algorithms, Vol. 15 (2022), Iss. 2 P.53

Intelligent reconstruction of unsteady combustion flow field of scramjet based on physical information constraints

Physics of Fluids, Vol. 36 (2024), Iss. 7

Physics-informed neural networks for learning fluid flows with symmetry

Korean Journal of Chemical Engineering, Vol. 40 (2023), Iss. 9 P.2119

Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm

Physics Letters A, Vol. 421 (2022), Iss. P.127739

Extended Physics-Informed Neural Networks for Solving Fluid Flow Problems in Highly Heterogeneous Media

Day 3 Wed, February 23, 2022, (2022),

Development of backward compatible physics-informed neural networks to reduce error accumulation based on a nested framework

Physics of Fluids, Vol. 36 (2024), Iss. 9

A critical review of physics-informed machine learning applications in subsurface energy systems

Geoenergy Science and Engineering, Vol. 239 (2024), Iss. P.212938

VC-PINN: Variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient

Physica D: Nonlinear Phenomena, Vol. 456 (2023), Iss. P.133945

Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

Advances in Computational Mathematics, Vol. 49 (2023), Iss. 4

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions

Neurocomputing, Vol. 468 (2022), Iss. P.165

Single Reference Frequency Loss for Multifrequency Wavefield Representation Using Physics-Informed Neural Networks

IEEE Geoscience and Remote Sensing Letters, Vol. 19 (2022), Iss. P.1

A DOMAIN-DECOMPOSED VAE METHOD FOR BAYESIAN INVERSE PROBLEMS

International Journal for Uncertainty Quantification, Vol. 14 (2024), Iss. 3 P.67

Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45 (2023), Iss. 12 P.15588

Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations

Journal of Computational Physics, Vol. 492 (2023), Iss. P.112415

Enhanced Surrogate Modelling of Heat Conduction Problems Using Physics-Informed Neural Network Framework

SSRN Electronic Journal , Vol. (2022), Iss.

A novel meta-learning initialization method for physics-informed neural networks

Neural Computing and Applications, Vol. 34 (2022), Iss. 17 P.14511

Physics‐informed learning of chemical reactor systems using decoupling–coupling training framework

AIChE Journal, Vol. 70 (2024), Iss. 7

Overview of the Application of Physically Informed Neural Networks to the Problems of Nonlinear Fluid Flow in Porous Media

Computation, Vol. 12 (2024), Iss. 4 P.69

Physics-Informed Neural Networks (PINNs)-Based Traffic State Estimation: An Application to Traffic Network

Algorithms, Vol. 15 (2022), Iss. 12 P.447

Surrogate-Based Physics-Informed Neural Networks for Elliptic Partial Differential Equations

Mathematics, Vol. 11 (2023), Iss. 12 P.2723

Residual-based adaptivity for two-phase flow simulation in porous media using Physics-informed Neural Networks

Computer Methods in Applied Mechanics and Engineering, Vol. 396 (2022), Iss. P.115100

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain

Computer Physics Communications, Vol. 295 (2024), Iss. P.109010

Quantitative Analysis of the Fractional Fokker–Planck–Levy Equation via a Modified Physics-Informed Neural Network Architecture

Fractal and Fractional, Vol. 8 (2024), Iss. 11 P.671

An improved physics-informed neural network with adaptive weighting and mixed differentiation for solving the incompressible Navier–Stokes equations

Nonlinear Dynamics, Vol. 112 (2024), Iss. 18 P.16113

Domain Decomposition Methods in Science and Engineering XXVII

Domain Decomposition Algorithms for Neural Network Approximation of Partial Differential Equations

2024

Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

Journal of Computational Physics, Vol. 490 (2023), Iss. P.112323

Loss landscape engineering via Data Regulation on PINNs

Machine Learning with Applications, Vol. 12 (2023), Iss. P.100464

Interfacial conditioning in physics informed neural networks

Physics of Fluids, Vol. 36 (2024), Iss. 7

Error estimates for physics-informed neural networks approximating the Navier–Stokes equations

IMA Journal of Numerical Analysis, Vol. 44 (2024), Iss. 1 P.83

Physics-informed shape optimization using coordinate projection

Scientific Reports, Vol. 14 (2024), Iss. 1

Iterative algorithms for partitioned neural network approximation to partial differential equations

Computers & Mathematics with Applications, Vol. 170 (2024), Iss. P.237

HOW IMPORTANT ARE ACTIVATION FUNCTIONS IN REGRESSION AND CLASSIFICATION? A SURVEY, PERFORMANCE COMPARISON, AND FUTURE DIRECTIONS

Journal of Machine Learning for Modeling and Computing, Vol. 4 (2023), Iss. 1 P.21

Finite volume-based supervised machine learning models for linear elastostatics

Advances in Engineering Software, Vol. 176 (2023), Iss. P.103390

Four-dimensional hemodynamic prediction of abdominal aortic aneurysms following endovascular aneurysm repair combining physics-informed PointNet and quadratic residual networks

Physics of Fluids, Vol. 36 (2024), Iss. 8

Utilizing symmetry-enhanced physics-informed neural network to obtain the solution beyond sampling domain for partial differential equations

Nonlinear Dynamics, Vol. 111 (2023), Iss. 23 P.21861

Combined analysis of thermofluids and electromagnetism using physics-informed neural networks

Engineering Applications of Artificial Intelligence, Vol. 133 (2024), Iss. P.108216

A novel physics-informed neural network for modeling electromagnetism of a permanent magnet synchronous motor

Advanced Engineering Informatics, Vol. 57 (2023), Iss. P.102035

Dynamical behavior and modulation instability of optical solitons in nonlinear directional couplers

Nonlinear Dynamics, Vol. 111 (2023), Iss. 11 P.10441

Advances in Hydroinformatics—SimHydro 2023 Volume 2

On the Application of Physics-Informed Neural Networks in the Modeling of Roll Waves

2024

Accelerated simulation methodologies for computational vascular flow modelling

Journal of The Royal Society Interface, Vol. 21 (2024), Iss. 211

Long-time integration of parametric evolution equations with physics-informed DeepONets

Journal of Computational Physics, Vol. 475 (2023), Iss. P.111855

Computational Science – ICCS 2024

Coupling PIES and PINN for Solving Two-Dimensional Boundary Value Problems via Domain Decomposition

2024

Reconstruction of hydrofoil cavitation flow based on the chain-style physics-informed neural network

Engineering Applications of Artificial Intelligence, Vol. 119 (2023), Iss. P.105724

Thermal fluid fields reconstruction for nanofluids convection based on physics-informed deep learning

Scientific Reports, Vol. 12 (2022), Iss. 1

The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity

Journal of Computational Physics, Vol. 451 (2022), Iss. P.110839

Machine learning and domain decomposition methods - a survey

Computational Science and Engineering, Vol. 1 (2024), Iss. 1

Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition

Hydrology and Earth System Sciences, Vol. 26 (2022), Iss. 16 P.4469

iPINNs: incremental learning for Physics-informed neural networks

Engineering with Computers, Vol. (2024), Iss.

Spatiotemporal parallel physics-informed neural networks: A framework to solve inverse problems in fluid mechanics

Physics of Fluids, Vol. 35 (2023), Iss. 6

Physics-informed deep learning approach for modeling crustal deformation

Nature Communications, Vol. 13 (2022), Iss. 1

Generalization of PINNs for elliptic interface problems

Applied Mathematics Letters, Vol. 157 (2024), Iss. P.109175

Deep learning in computational mechanics: a review

Computational Mechanics, Vol. 74 (2024), Iss. 2 P.281

Solving Benjamin–Ono equation via gradient balanced PINNs approach

The European Physical Journal Plus, Vol. 137 (2022), Iss. 7

Data driven multi soliton solutions of the Fokas-Lenells equation using conservation laws in PINN

Journal of Optics, Vol. (2024), Iss.

PINN enhanced extended multiscale finite element method for fast mechanical analysis of heterogeneous materials

Acta Mechanica, Vol. 235 (2024), Iss. 7 P.4895

Physics-constrained neural networks as multi-material Riemann solvers for compressible two-gas simulations

Journal of Computational Science, Vol. 78 (2024), Iss. P.102261

A Hard Constraint and Domain-Decomposition- Based Physics-Informed Neural Network Framework for Nonhomogeneous Transient Thermal Analysis

IEEE Transactions on Components, Packaging and Manufacturing Technology, Vol. 14 (2024), Iss. 7 P.1215

Data-driven vector degenerate and nondegenerate solitons of coupled nonlocal nonlinear Schrödinger equation via improved PINN algorithm

Nonlinear Dynamics, Vol. (2024), Iss.

Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations

Computer Physics Communications, Vol. 299 (2024), Iss. P.109130

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

Soft Matter, Vol. 18 (2022), Iss. 1 P.172

Investigating the Dynamics of a Unidirectional Wave Model: Soliton Solutions, Bifurcation, and Chaos Analysis

Fractal and Fractional, Vol. 8 (2024), Iss. 11 P.672

Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation

Entropy, Vol. 24 (2022), Iss. 8 P.1106

The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue waves

Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 34 (2024), Iss. 3

Multi-parallelized PINNs for the inverse problem study of NLS typed equations in optical fiber communications: Discovery on diverse high-order terms and variable coefficients

Chaos, Solitons & Fractals, Vol. 181 (2024), Iss. P.114595

A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems

Multiscale Science and Engineering, Vol. 6 (2024), Iss. 1 P.1

Adaptive loss weighting auxiliary output fPINNs for solving fractional partial integro-differential equations

Physica D: Nonlinear Phenomena, Vol. 460 (2024), Iss. P.134066

Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations

Computers & Fluids, Vol. 241 (2022), Iss. P.105481

Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity

Neural Networks, Vol. 162 (2023), Iss. P.472

Investigating deep energy method applications in thermoelasticity

Engineering Analysis with Boundary Elements, Vol. 159 (2024), Iss. P.302

Adaptive fractional physical information neural network based on PQI scheme for solving time-fractional partial differential equations

Electronic Research Archive, Vol. 32 (2024), Iss. 4 P.2699

Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation

Applied Sciences, Vol. 14 (2024), Iss. 16 P.7002

A piecewise extreme learning machine for interface problems

Mathematics and Computers in Simulation, Vol. 227 (2025), Iss. P.303

Machine learning for polymer composites process simulation – a review

Composites Part B: Engineering, Vol. 246 (2022), Iss. P.110208

Physics-guided Bayesian neural networks by ABC-SS: Application to reinforced concrete columns

Engineering Applications of Artificial Intelligence, Vol. 119 (2023), Iss. P.105790

GPINN with Neural Tangent Kernel Technique for Nonlinear Two Point Boundary Value Problems

Neural Processing Letters, Vol. 56 (2024), Iss. 3

Perspectives on the integration between first-principles and data-driven modeling

Computers & Chemical Engineering, Vol. 166 (2022), Iss. P.107898

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

Computer Methods in Applied Mechanics and Engineering, Vol. 391 (2022), Iss. P.114587

Stochastic projection based approach for gradient free physics informed learning

Computer Methods in Applied Mechanics and Engineering, Vol. 406 (2023), Iss. P.115842

Physics-informed machine learning

Nature Reviews Physics, Vol. 3 (2021), Iss. 6 P.422

Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity

Computer Methods in Applied Mechanics and Engineering, Vol. 431 (2024), Iss. P.117268

Scalable algorithms for physics-informed neural and graph networks

Data-Centric Engineering, Vol. 3 (2022), Iss.

Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks

Buildings, Vol. 14 (2024), Iss. 10 P.3215

Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN)

Journal of Computational Physics, Vol. 479 (2023), Iss. P.112003

Physics-informed neural networks for inverse problems in supersonic flows

Journal of Computational Physics, Vol. 466 (2022), Iss. P.111402

Binary structured physics-informed neural networks for solving equations with rapidly changing solutions

Journal of Computational Physics, Vol. 518 (2024), Iss. P.113341

Feature Engineering with Regularity Structures

Journal of Scientific Computing, Vol. 98 (2024), Iss. 1

AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena

International Journal of Digital Earth, Vol. 17 (2024), Iss. 1

Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions

Journal of Computational Physics, Vol. 504 (2024), Iss. P.112856

Drift-Correcting Multiphysics Informed Neural Network Coupled PDE Solver

IEEE Journal on Multiscale and Multiphysics Computational Techniques, Vol. 9 (2024), Iss. P.303

Variable coefficient-informed neural network for PDE inverse problem in fluid dynamics

Physica D: Nonlinear Phenomena, Vol. (2024), Iss. P.134362

Physics-Informed Deep Neural Network for Inhomogeneous Magnetized Plasma Parameter Inversion

IEEE Antennas and Wireless Propagation Letters, Vol. 21 (2022), Iss. 4 P.828

Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review

Archives of Computational Methods in Engineering, Vol. (2024), Iss.

Deep learning methods for partial differential equations and related parameter identification problems

Inverse Problems, Vol. 39 (2023), Iss. 10 P.103001

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Journal of Computational Physics, Vol. 477 (2023), Iss. P.111902

Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation

Journal of Computational Physics, Vol. 473 (2023), Iss. P.111768

Turbulence model augmented physics-informed neural networks for mean-flow reconstruction

Physical Review Fluids, Vol. 9 (2024), Iss. 3

Predicting the Early-Age Time-Dependent Behaviors of a Prestressed Concrete Beam by Using Physics-Informed Neural Network

Sensors, Vol. 23 (2023), Iss. 14 P.6649

A-PINN: Auxiliary Physics Informed Neural Networks for Forward and Inverse Problems of Nonlinear Integro-Differential Equations

SSRN Electronic Journal , Vol. (2022), Iss.

Advanced Physics-informed neural networks for numerical approximation of the coupled Schrödinger–KdV equation

Communications in Nonlinear Science and Numerical Simulation, Vol. 138 (2024), Iss. P.108229

Navigating PINNs via maximum residual-based continuous distribution

Communications in Nonlinear Science and Numerical Simulation, Vol. (2024), Iss. P.108460

Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions

Computer Methods in Applied Mechanics and Engineering, Vol. 429 (2024), Iss. P.117168

Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations

Chaos, Solitons & Fractals, Vol. 189 (2024), Iss. P.115658

Dynamic analysis on optical pulses via modified PINNs: Soliton solutions, rogue waves and parameter discovery of the CQ-NLSE

Communications in Nonlinear Science and Numerical Simulation, Vol. 126 (2023), Iss. P.107441

Multiple scattering simulation via physics-informed neural networks

Engineering with Computers, Vol. (2024), Iss.

Variationally mimetic operator networks

Computer Methods in Applied Mechanics and Engineering, Vol. 419 (2024), Iss. P.116536

Parametric deep energy approach for elasticity accounting for strain gradient effects

Computer Methods in Applied Mechanics and Engineering, Vol. 386 (2021), Iss. P.114096

GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity

Machine Learning: Science and Technology, Vol. 5 (2024), Iss. 2 P.025014

A peridynamic-informed neural network for continuum elastic displacement characterization

Computer Methods in Applied Mechanics and Engineering, Vol. 407 (2023), Iss. P.115909

Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing

Engineering with Computers, Vol. 39 (2023), Iss. 4 P.2961

Evaluating single multiplicative neuron models in physics-informed neural networks for differential equations

Agraz, Melih

Scientific Reports, Vol. 14 (2024), Iss. 1

Self-supervised learning of hologram reconstruction using physics consistency

Nature Machine Intelligence, Vol. 5 (2023), Iss. 8 P.895

TransFlowNet: A physics-constrained Transformer framework for spatio-temporal super-resolution of flow simulations

Journal of Computational Science, Vol. 65 (2022), Iss. P.101906

Physics-infused deep neural network for solution of non-associative Drucker–Prager elastoplastic constitutive model

Journal of the Mechanics and Physics of Solids, Vol. 185 (2024), Iss. P.105570

A Novel Neural-Network Device Modeling Based on Physics-Informed Machine Learning

IEEE Transactions on Electron Devices, Vol. 70 (2023), Iss. 11 P.6021

Orthogonal grid physics-informed neural networks: A neural network-based simulation tool for advection–diffusion–reaction problems

Physics of Fluids, Vol. 34 (2022), Iss. 7

Boundary integrated neural networks for 2D elastostatic and piezoelectric problems

International Journal of Mechanical Sciences, Vol. 280 (2024), Iss. P.109525

Deep learning phase‐field model for brittle fractures

International Journal for Numerical Methods in Engineering, Vol. 124 (2023), Iss. 3 P.620

Repeated antiderivative transformation iterative deep learning solution method (R-AIM) for multi-dimensional continuity integral equations

SCIENTIA SINICA Mathematica, Vol. (2024), Iss.

ro-PINN: A reduced order physics-informed neural network for solving the macroscopic model of pedestrian flows

Transportation Research Part C: Emerging Technologies, Vol. 163 (2024), Iss. P.104658

Prediction of phase transition and time-varying dynamics of the (2+1) -dimensional Boussinesq equation by parameter-integrated physics-informed neural networks with phase domain decomposition

Physical Review E, Vol. 108 (2023), Iss. 4

Recent advances and prospects in hypersonic inlet design and intelligent optimization

Aerospace Science and Technology, Vol. 146 (2024), Iss. P.108953

Application of Physics-Informed Neural Networks for forward and inverse analysis of pile–soil interaction

International Journal of Solids and Structures, Vol. 277-278 (2023), Iss. P.112319

Improved Deep Neural Networks with Domain Decomposition in Solving Partial Differential Equations

Journal of Scientific Computing, Vol. 93 (2022), Iss. 1

A physics-informed deep convolutional neural network for simulating and predicting transient Darcy flows in heterogeneous reservoirs without labeled data

Zhang, Zhao

Journal of Petroleum Science and Engineering, Vol. 211 (2022), Iss. P.110179

The modified physics-informed neural network (PINN) method for the thermoelastic wave propagation analysis based on the Moore-Gibson-Thompson theory in porous materials

Composite Structures, Vol. 348 (2024), Iss. P.118485

Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: An application to rubber calendering process

Engineering Applications of Artificial Intelligence, Vol. 114 (2022), Iss. P.105176

Parallel physics-informed neural networks via domain decomposition

Journal of Computational Physics, Vol. 447 (2021), Iss. P.110683

Data-driven rogue waves solutions for the focusing and variable coefficient nonlinear Schrödinger equations via deep learning

Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 34 (2024), Iss. 7

Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions

Journal of Geophysical Research: Solid Earth, Vol. 127 (2022), Iss. 5

An augmented physics informed neural network approach for blunt-body dynamics

Physics of Fluids, Vol. 36 (2024), Iss. 1

NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs

Journal of Computational Physics, Vol. 496 (2024), Iss. P.112603

Physics informed neural networks for continuum micromechanics

Computer Methods in Applied Mechanics and Engineering, Vol. 393 (2022), Iss. P.114790

A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations

Physica D: Nonlinear Phenomena, Vol. 443 (2023), Iss. P.133562

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

SIAM Journal on Scientific Computing, Vol. 44 (2022), Iss. 5 P.A3158

Multifidelity modeling for Physics-Informed Neural Networks (PINNs)

Journal of Computational Physics, Vol. 451 (2022), Iss. P.110844

Multilevel domain decomposition-based architectures for physics-informed neural networks

Computer Methods in Applied Mechanics and Engineering, Vol. 429 (2024), Iss. P.117116

Recent progress on mechanics investigations of heterogeneous materials based on physical information neural networks

Chinese Science Bulletin, Vol. (2024), Iss.

A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures

Computers & Structures, Vol. 265 (2022), Iss. P.106761

Self-adaptive loss balanced Physics-informed neural networks

Neurocomputing, Vol. 496 (2022), Iss. P.11

Deep Learning Nonhomogeneous Elliptic Interface Problems by Soft Constraint Physics-Informed Neural Networks

Mathematics, Vol. 11 (2023), Iss. 8 P.1843

The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering

Industrial & Engineering Chemistry Research, Vol. 62 (2023), Iss. 44 P.18178

Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy

Algorithms, Vol. 17 (2024), Iss. 9 P.415

A Second-Order Network Structure Based on Gradient-Enhanced Physics-Informed Neural Networks for Solving Parabolic Partial Differential Equations

Entropy, Vol. 25 (2023), Iss. 4 P.674

Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset

Energies, Vol. 14 (2021), Iss. 16 P.5127

An extreme learning machine-based method for computational PDEs in higher dimensions

Computer Methods in Applied Mechanics and Engineering, Vol. 418 (2024), Iss. P.116578

Optimal parameter estimation for uncertain structural systems under unknown random excitations

Journal of Vibration and Control, Vol. (2024), Iss.

Reconstructing unsaturated infiltration behavior with sparse data via physics-informed deep learning

Computers and Geotechnics, Vol. 168 (2024), Iss. P.106162

Accelerating heat exchanger design by combining physics-informed deep learning and transfer learning

Chemical Engineering Science, Vol. 282 (2023), Iss. P.119285

CoDBench: a critical evaluation of data-driven models for continuous dynamical systems

Digital Discovery, Vol. 3 (2024), Iss. 6 P.1172

Physics-informed neural network based on a new adaptive gradient descent algorithm for solving partial differential equations of flow problems

Physics of Fluids, Vol. 35 (2023), Iss. 6

Recent Advances in Neuromorphic Computing [Working Title]

Physics-informed neural network: principles and applications

2024

Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

Applied Sciences, Vol. 14 (2024), Iss. 13 P.5490

Hutchinson Trace Estimation for high-dimensional and high-order Physics-Informed Neural Networks

Computer Methods in Applied Mechanics and Engineering, Vol. 424 (2024), Iss. P.116883

A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations

Computer Methods in Applied Mechanics and Engineering, Vol. 390 (2022), Iss. P.114474

Physics-Informed Deep Learning for Computational Elastodynamics without Labeled Data

Journal of Engineering Mechanics, Vol. 147 (2021), Iss. 8

Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network

Journal of Energy Storage, Vol. 60 (2023), Iss. P.106654

Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks

Experiments in Fluids, Vol. 64 (2023), Iss. 5

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

Proceedings of the National Academy of Sciences, Vol. 121 (2024), Iss. 2

Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations

Physica D: Nonlinear Phenomena, Vol. 459 (2024), Iss. P.134023

Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem

Computers & Mathematics with Applications, Vol. 150 (2023), Iss. P.229

A nonlocal energy-informed neural network based on peridynamics for elastic solids with discontinuities

Computational Mechanics, Vol. 73 (2024), Iss. 2 P.233

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

Neurocomputing, Vol. 535 (2023), Iss. P.166

Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations

Journal of Computational Physics, Vol. 505 (2024), Iss. P.112917

Mean flow reconstruction of unsteady flows using physics-informed neural networks

Data-Centric Engineering, Vol. 4 (2023), Iss.

Machine Learning, Optimization, and Data Science

Deep Learning Model of Two-Phase Fluid Transport Through Fractured Media: A Real-World Case Study

2024

Physics-Informed Neural Networks for Heat Transfer Problems

Journal of Heat Transfer, Vol. 143 (2021), Iss. 6

A symmetry group based supervised learning method for solving partial differential equations

Computer Methods in Applied Mechanics and Engineering, Vol. 414 (2023), Iss. P.116181

A computational framework for the indirect estimation of interface thermal resistance of composite materials using XPINNs

International Journal of Heat and Mass Transfer, Vol. 200 (2023), Iss. P.123420

SSNO: Spatio-Spectral Neural Operator for Functional Space Learning of Partial Differential Equations

IEEE Access, Vol. 10 (2022), Iss. P.15084

A Hybrid Neural Network and Data Sampling Solver for Forward and Inverse Modified Diffusion Equations

SSRN Electronic Journal , Vol. (2022), Iss.

Adaptive sampling points based multi-scale residual network for solving partial differential equations

Computers & Mathematics with Applications, Vol. 169 (2024), Iss. P.223

The variational physics-informed neural networks for time-fractional nonlinear conservation laws

IFAC-PapersOnLine, Vol. 58 (2024), Iss. 12 P.472

Physical phenomena analysis of solution structures in a nonlinear electric transmission network with dissipative elements

The European Physical Journal Plus, Vol. 139 (2024), Iss. 1

Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks

Network: Computation in Neural Systems, Vol. (2024), Iss. P.1

Solving Constrained Pseudoconvex Optimization Problems with deep learning-based neurodynamic optimization

Mathematics and Computers in Simulation, Vol. 219 (2024), Iss. P.424

Deep learning techniques applied to super-resolution chemistry transport modeling for operational uses

Environmental Research Communications, Vol. 3 (2021), Iss. 8 P.085001

Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones

Chaos, Solitons & Fractals, Vol. 176 (2023), Iss. P.114090

Bright-dark rogue wave transition in coupled AB system via the physics-informed neural networks method

Communications in Applied Mathematics and Computational Science, Vol. 19 (2024), Iss. 1 P.1

Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators

Physics of Fluids, Vol. 36 (2024), Iss. 3

A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation

Physics of Fluids, Vol. 36 (2024), Iss. 2