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Non-intrusive reduced order modeling of poroelasticity of heterogeneous
  media based on a discontinuous Galerkin approximation

Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation

28 January 2021
T. Kadeethum
F. Ballarin
N. Bouklas
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation"

7 / 7 papers shown
Efficient machine-learning surrogates for large-scale geological carbon
  and energy storage
Efficient machine-learning surrogates for large-scale geological carbon and energy storage
T. Kadeethum
Stephen J Verzi
Hongkyu Yoon
AI4CE
162
2
0
11 Oct 2023
Stochastic Data-Driven Variational Multiscale Reduced Order Models
Stochastic Data-Driven Variational Multiscale Reduced Order Models
Fei Lu
Changhong Mou
Honghu Liu
T. Iliescu
136
1
0
06 Sep 2022
Reduced order modeling for flow and transport problems with Barlow Twins
  self-supervised learning
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learningScientific Reports (Sci Rep), 2022
T. Kadeethum
F. Ballarin
Daniel O’Malley
Youngsoo Choi
N. Bouklas
H. Yoon
AI4CE
188
20
0
11 Feb 2022
Non-intrusive reduced order modeling of natural convection in porous
  media using convolutional autoencoders: comparison with linear subspace
  techniques
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniquesAdvances in Water Resources (Adv. Water Resour.), 2021
T. Kadeethum
F. Ballarin
Y. Cho
Daniel O’Malley
H. Yoon
N. Bouklas
AI4CE
169
68
0
23 Jul 2021
Interval and fuzzy physics-informed neural networks for uncertain fields
Interval and fuzzy physics-informed neural networks for uncertain fieldsProbabilistic Engineering Mechanics (PEM), 2021
J. Fuhg
Ioannis Kalogeris
A. Fau
N. Bouklas
AI4CE
213
21
0
18 Jun 2021
A framework for data-driven solution and parameter estimation of PDEs
  using conditional generative adversarial networks
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networksNature Computational Science (Nat. Comput. Sci.), 2021
T. Kadeethum
Daniel O’Malley
J. Fuhg
Youngsoo Choi
Jonghyun Lee
Hari S. Viswanathan
N. Bouklas
AI4CE
157
97
0
27 May 2021
The mixed deep energy method for resolving concentration features in
  finite strain hyperelasticity
The mixed deep energy method for resolving concentration features in finite strain hyperelasticityJournal of Computational Physics (JCP), 2021
J. Fuhg
N. Bouklas
PINNAI4CE
148
123
0
15 Apr 2021
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