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Thermodynamically Consistent Machine-Learned Internal State Variable
  Approach for Data-Driven Modeling of Path-Dependent Materials

Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials

1 May 2022
Xiaolong He
Jiun-Shyan Chen
    AI4CE
ArXivPDFHTML

Papers citing "Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials"

11 / 11 papers shown
Title
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen
Andreas Fürst
D. Tunuguntla
Daniel N. Wilke
Benedikt Alkin
...
Takayuki Shuku
WaiChing Sun
T. Weinhart
Dongwei Ye
Hongyang Cheng
AI4CE
31
0
0
01 Apr 2025
A thermodynamically consistent physics-informed deep learning material
  model for short fiber/polymer nanocomposites
A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
Betim Bahtiri
B. Arash
Sven Scheffler
Maximilian Jux
R. Rolfes
AI4CE
28
5
0
27 Mar 2024
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for
  Boundary Value Problems
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
Jonghyuk Baek
Yanran Wang
J. S. Chen
30
1
0
16 Jan 2024
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under
  High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle
  Method
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method
Jonghyuk Baek
Yanran Wang
Xiaolong He
Yu Lu
J. McCartney
J. S. Chen
26
0
0
24 Sep 2023
Discovering interpretable elastoplasticity models via the neural
  polynomial method enabled symbolic regressions
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
B. Bahmani
H. S. Suh
WaiChing Sun
23
17
0
24 Jul 2023
A Neural Network-Based Enrichment of Reproducing Kernel Approximation
  for Modeling Brittle Fracture
A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture
Jonghyuk Baek
Jiun-Shyan Chen
25
12
0
04 Jul 2023
A Multi-Resolution Physics-Informed Recurrent Neural Network:
  Formulation and Application to Musculoskeletal Systems
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems
Karan Taneja
Xiaolong He
Qizhi He
Jiun-Shyan Chen
41
8
0
26 May 2023
A machine learning-based viscoelastic-viscoplastic model for epoxy
  nanocomposites with moisture content
A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content
Betim Bahtiri
B. Arash
Sven Scheffler
Maximilian Jux
R. Rolfes
28
10
0
14 May 2023
Learning solution of nonlinear constitutive material models using
  physics-informed neural networks: COMM-PINN
Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN
Shahed Rezaei
Ahmad Moeineddin
Ali Harandi
PINN
32
18
0
10 Apr 2023
LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling
  of Short Fiber-Reinforced Composites
LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites
Haoyan Wei
Chengtang Wu
Wei Hu
Tung-Huan Su
Hitoshi Oura
M. Nishi
T. Naito
Stan Chung
Leo Shen
AI4CE
15
17
0
06 Jan 2023
Deep autoencoders for physics-constrained data-driven nonlinear
  materials modeling
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Xiaolong He
Qizhi He
Jiun-Shyan Chen
AI4CE
PINN
SyDa
42
56
0
03 Sep 2022
1