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On the convergence of physics informed neural networks for linear
  second-order elliptic and parabolic type PDEs

On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs

3 April 2020
Yeonjong Shin
Jérome Darbon
George Karniadakis
    PINN
ArXivPDFHTML

Papers citing "On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs"

37 / 37 papers shown
Title
Label Propagation Training Schemes for Physics-Informed Neural Networks
  and Gaussian Processes
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
Ming Zhong
Dehao Liu
Raymundo Arroyave
U. Braga-Neto
AI4CE
SSL
26
1
0
08 Apr 2024
Approximation of Solution Operators for High-dimensional PDEs
Approximation of Solution Operators for High-dimensional PDEs
Nathan Gaby
Xiaojing Ye
30
0
0
18 Jan 2024
Moving Sampling Physics-informed Neural Networks induced by Moving Mesh
  PDE
Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
Yu Yang
Qihong Yang
Yangtao Deng
Qiaolin He
16
3
0
14 Nov 2023
Neural oscillators for generalization of physics-informed machine
  learning
Neural oscillators for generalization of physics-informed machine learning
Taniya Kapoor
Abhishek Chandra
D. Tartakovsky
Hongrui Wang
Alfredo Núñez
R. Dollevoet
AI4CE
32
11
0
17 Aug 2023
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based
  Partial Differential Equations Solving
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Junjun Yan
Xinhai Chen
Zhichao Wang
Enqiang Zhou
Jie Liu
PINN
AI4CE
31
1
0
12 Jul 2023
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial
  Differential Equations
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
Junjun Yan
Xinhai Chen
Zhichao Wang
Enqiang Zhoui
Jie Liu
PINN
DiffM
AI4CE
32
10
0
15 Jun 2023
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
Yuling Jiao
Yanming Lai
Yang Wang
Haizhao Yang
Yunfei Yang
26
3
0
05 Feb 2023
Neural Control of Parametric Solutions for High-dimensional Evolution
  PDEs
Neural Control of Parametric Solutions for High-dimensional Evolution PDEs
Nathan Gaby
X. Ye
Haomin Zhou
19
6
0
31 Jan 2023
Physics-Informed Neural Network Method for Parabolic Differential
  Equations with Sharply Perturbed Initial Conditions
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
Yifei Zong
Qizhi He
A. Tartakovsky
PINN
22
18
0
18 Aug 2022
wPINNs: Weak Physics informed neural networks for approximating entropy
  solutions of hyperbolic conservation laws
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
Tim De Ryck
Siddhartha Mishra
Roberto Molinaro
PINN
40
29
0
18 Jul 2022
Informed Learning by Wide Neural Networks: Convergence, Generalization
  and Sampling Complexity
Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity
Jianyi Yang
Shaolei Ren
35
3
0
02 Jul 2022
Generic bounds on the approximation error for physics-informed (and)
  operator learning
Generic bounds on the approximation error for physics-informed (and) operator learning
Tim De Ryck
Siddhartha Mishra
PINN
63
59
0
23 May 2022
Error estimates for physics informed neural networks approximating the
  Navier-Stokes equations
Error estimates for physics informed neural networks approximating the Navier-Stokes equations
Tim De Ryck
Ameya Dilip Jagtap
S. Mishra
PINN
49
115
0
17 Mar 2022
Physics-informed neural networks for inverse problems in supersonic
  flows
Physics-informed neural networks for inverse problems in supersonic flows
Ameya Dilip Jagtap
Zhiping Mao
Nikolaus Adams
George Karniadakis
PINN
26
201
0
23 Feb 2022
Learning Physics-Informed Neural Networks without Stacked
  Back-propagation
Learning Physics-Informed Neural Networks without Stacked Back-propagation
Di He
Shanda Li
Wen-Wu Shi
Xiaotian Gao
Jia Zhang
Jiang Bian
Liwei Wang
Tie-Yan Liu
DiffM
PINN
AI4CE
18
23
0
18 Feb 2022
State-of-the-Art Review of Design of Experiments for Physics-Informed
  Deep Learning
State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning
Sourav Das
S. Tesfamariam
PINN
AI4CE
26
19
0
13 Feb 2022
Solving time dependent Fokker-Planck equations via temporal normalizing
  flow
Solving time dependent Fokker-Planck equations via temporal normalizing flow
Xiaodong Feng
Li Zeng
Tao Zhou
AI4CE
36
25
0
28 Dec 2021
Physics informed neural networks for continuum micromechanics
Physics informed neural networks for continuum micromechanics
Alexander Henkes
Henning Wessels
R. Mahnken
PINN
AI4CE
32
139
0
14 Oct 2021
Error analysis for physics informed neural networks (PINNs)
  approximating Kolmogorov PDEs
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs
Tim De Ryck
Siddhartha Mishra
PINN
26
100
0
28 Jun 2021
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Ziang Chen
Jianfeng Lu
Yulong Lu
38
27
0
14 Jun 2021
Accelerating Dynamical System Simulations with Contracting and
  Physics-Projected Neural-Newton Solvers
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
Samuel C. Chevalier
Jochen Stiasny
Spyros Chatzivasileiadis
33
3
0
04 Jun 2021
Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal
  Transport based Density Estimation
Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal Transport based Density Estimation
Amanpreet Singh
Martin Bauer
S. Joshi
OT
6
1
0
02 Apr 2021
Physics-informed neural networks for the shallow-water equations on the
  sphere
Physics-informed neural networks for the shallow-water equations on the sphere
Alexander Bihlo
R. Popovych
14
77
0
01 Apr 2021
Solving and Learning Nonlinear PDEs with Gaussian Processes
Solving and Learning Nonlinear PDEs with Gaussian Processes
Yifan Chen
Bamdad Hosseini
H. Owhadi
Andrew M. Stuart
34
153
0
24 Mar 2021
A Deep Learning approach to Reduced Order Modelling of Parameter
  Dependent Partial Differential Equations
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
N. R. Franco
Andrea Manzoni
P. Zunino
33
45
0
10 Mar 2021
Reproducing Activation Function for Deep Learning
Reproducing Activation Function for Deep Learning
Senwei Liang
Liyao Lyu
Chunmei Wang
Haizhao Yang
36
21
0
13 Jan 2021
Hybrid FEM-NN models: Combining artificial neural networks with the
  finite element method
Hybrid FEM-NN models: Combining artificial neural networks with the finite element method
Sebastian K. Mitusch
S. Funke
M. Kuchta
AI4CE
44
94
0
04 Jan 2021
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
O. Hennigh
S. Narasimhan
M. A. Nabian
Akshay Subramaniam
Kaustubh Tangsali
M. Rietmann
J. Ferrandis
Wonmin Byeon
Z. Fang
S. Choudhry
PINN
AI4CE
93
126
0
14 Dec 2020
Energy-based error bound of physics-informed neural network solutions in
  elasticity
Energy-based error bound of physics-informed neural network solutions in elasticity
Mengwu Guo
E. Haghighat
PINN
56
28
0
18 Oct 2020
Large-scale Neural Solvers for Partial Differential Equations
Large-scale Neural Solvers for Partial Differential Equations
Patrick Stiller
Friedrich Bethke
M. Böhme
R. Pausch
Sunna Torge
A. Debus
J. Vorberger
Michael Bussmann
Nico Hoffmann
AI4CE
16
26
0
08 Sep 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention
  Mechanism
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
L. McClenny
U. Braga-Neto
PINN
28
446
0
07 Sep 2020
When and why PINNs fail to train: A neural tangent kernel perspective
When and why PINNs fail to train: A neural tangent kernel perspective
Sizhuang He
Xinling Yu
P. Perdikaris
33
881
0
28 Jul 2020
Estimates on the generalization error of Physics Informed Neural
  Networks (PINNs) for approximating a class of inverse problems for PDEs
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating a class of inverse problems for PDEs
Siddhartha Mishra
Roberto Molinaro
PINN
16
262
0
29 Jun 2020
Estimates on the generalization error of Physics Informed Neural
  Networks (PINNs) for approximating PDEs
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs
Siddhartha Mishra
Roberto Molinaro
PINN
30
171
0
29 Jun 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes
  Equations using Finite Volume Discretization
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization
Rishikesh Ranade
C. Hill
Jay Pathak
AI4CE
59
123
0
17 May 2020
Model Reduction and Neural Networks for Parametric PDEs
Model Reduction and Neural Networks for Parametric PDEs
K. Bhattacharya
Bamdad Hosseini
Nikola B. Kovachki
Andrew M. Stuart
24
319
0
07 May 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain
  Decomposition
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
E. Kharazmi
Zhongqiang Zhang
George Karniadakis
135
510
0
11 Mar 2020
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