Stiff-PDEs and Physics-Informed Neural Networks

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

(2022) Han Gao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-informed neural networks and functional interpolation for stiff chemical kinetics

(2022) Mario De Florio et al.   CHAOS

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

(2022) Jeremy Yu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-Informed Neural Networks for rarefied-gas dynamics: Poiseuille flow in the BGK approximation

(2022) Mario De Florio et al.   ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND PHYSIK

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

(2022) Maryam Aliakbari et al.   INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW

Investigating molecular transport in the human brain from MRI with physics-informed neural networks

(2022) Bastian Zapf et al.   Scientific Reports

Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

(2022) Chen Xu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Enhanced physics‐informed neural networks for hyperelasticity

(2022) Diab W. Abueidda et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

MIONet: Learning Multiple-Input Operators via Tensor Product

(2022) Pengzhan Jin et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

DLBench: a comprehensive experimental evaluation of deep learning frameworks

(2021) Radwa Elshawi et al.   Cluster Computing

Efficient training of physics‐informed neural networks via importance sampling

(2021) Mohammad Amin Nabian et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

(2021) Colby L. Wight & Jia Zhao   Communications in Computational Physics

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

(2021) Shengze Cai et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Uncovering near-wall blood flow from sparse data with physics-informed neural networks

(2021) Amirhossein Arzani et al.   PHYSICS OF FLUIDS

Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks

(2021) N. Sukumar et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

(2021) Sifan Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

When and why PINNs fail to train: A neural tangent kernel perspective

(2021) Sifan Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators

(2021) Zhiping Mao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations

(2021) Enrico Schiassi et al.   NEUROCOMPUTING

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

(2021) Sifan Wang et al.   Science Advances

Extraction of mechanical properties of materials through deep learning from instrumented indentation

(2020) Lu Lu et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

(2020) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A review of automatic differentiation and its efficient implementation

(2019) Charles C. Margossian   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

(2019) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

(2019) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Sigmoid-weighted linear units for neural network function approximation in reinforcement learning

(2018) Stefan Elfwing et al.   NEURAL NETWORKS

Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations

(2018) Maziar Raissi et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep learning of vortex-induced vibrations

(2018) Maziar Raissi et al.   JOURNAL OF FLUID MECHANICS

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

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep Reinforcement Learning: A Brief Survey

(2017) Kai Arulkumaran et al.   IEEE SIGNAL PROCESSING MAGAZINE

Effective sample size for importance sampling based on discrepancy measures

(2017) Luca Martino et al.   SIGNAL PROCESSING

Development and Application of Artificial Neural Network

(2017) Yu-chen Wu et al.   WIRELESS PERSONAL COMMUNICATIONS

A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks

(2015) Keith Rudd et al.   NEUROCOMPUTING

A Tutorial on Latin Hypercube Design of Experiments

(2015) Felipe A. C. Viana   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Generalized Halton sequences in 2008

(2009) Henri Faure et al.   ACM Transactions on Modeling and Computer Simulation

Constructing Sobol Sequences with Better Two-Dimensional Projections

(2008) Stephen Joe et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING