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A mixed formulation for physics-informed neural networks as a potential
  solver for engineering problems in heterogeneous domains: comparison with
  finite element method

A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

27 June 2022
Shahed Rezaei
Ali Harandi
Ahmad Moeineddin
Bai-Xiang Xu
Stefanie Reese
ArXivPDFHTML

Papers citing "A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method"

9 / 9 papers shown
Title
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
Tengfei Xing
Xiaodan Ren
Jie Li
DiffM
32
0
0
26 Apr 2025
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information
Tengfei Xing
Xiaodan Ren
Jie Li
38
1
0
26 Apr 2025
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
Hamidreza Eivazi
Jendrik-Alexander Tröger
Stefan H. A. Wittek
Stefan Hartmann
Andreas Rausch
AI4CE
41
0
0
27 Mar 2025
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems
Bocheng Zeng
Qi Wang
M. Yan
Y. Liu
Ruizhi Chengze
Yi Zhang
Hongsheng Liu
Zidong Wang
Hao Sun
AI4CE
21
3
0
02 Oct 2024
A finite element-based physics-informed operator learning framework for
  spatiotemporal partial differential equations on arbitrary domains
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
Yusuke Yamazaki
Ali Harandi
Mayu Muramatsu
A. Viardin
Markus Apel
T. Brepols
Stefanie Reese
Shahed Rezaei
AI4CE
26
12
0
21 May 2024
Solving Elliptic Problems with Singular Sources using Singularity
  Splitting Deep Ritz Method
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method
Tianhao Hu
Bangti Jin
Zhi Zhou
21
6
0
07 Sep 2022
Stress field prediction in fiber-reinforced composite materials using a
  deep learning approach
Stress field prediction in fiber-reinforced composite materials using a deep learning approach
Anindya Bhaduri
Ashwini Gupta
L. Graham‐Brady
AI4CE
14
114
0
01 Nov 2021
Bayesian neural networks for weak solution of PDEs with uncertainty
  quantification
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
Xiaoxuan Zhang
K. Garikipati
AI4CE
24
11
0
13 Jan 2021
An Energy Approach to the Solution of Partial Differential Equations in
  Computational Mechanics via Machine Learning: Concepts, Implementation and
  Applications
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
E. Samaniego
C. Anitescu
S. Goswami
Vien Minh Nguyen-Thanh
Hongwei Guo
Khader M. Hamdia
Timon Rabczuk
X. Zhuang
PINN
AI4CE
145
1,333
0
27 Aug 2019
1