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Efficient Uncertainty Quantification for Dynamic Subsurface Flow with
  Surrogate by Theory-guided Neural Network

Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network

25 April 2020
Nanzhe Wang
Haibin Chang
Dongxiao Zhang
ArXivPDFHTML

Papers citing "Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network"

4 / 4 papers shown
Title
Neural Operator-Based Proxy for Reservoir Simulations Considering
  Varying Well Settings, Locations, and Permeability Fields
Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields
Daniel Badawi
Eduardo Gildin
OOD
AI4CE
11
4
0
13 Jul 2024
Uncertainty quantification of two-phase flow in porous media via
  coupled-TgNN surrogate model
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model
Jun Yu Li
Dongxiao Zhang
Tianhao He
Q. Zheng
AI4CE
17
6
0
28 May 2022
Use of Multifidelity Training Data and Transfer Learning for Efficient
  Construction of Subsurface Flow Surrogate Models
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models
Su Jiang
L. Durlofsky
AI4CE
11
29
0
23 Apr 2022
Theory-guided Auto-Encoder for Surrogate Construction and Inverse
  Modeling
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling
Nanzhe Wang
Haibin Chang
Dongxiao Zhang
AI4CE
15
48
0
17 Nov 2020
1