AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems-Reference-Cited by-同舟云学术

AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems

Published:2022-06-23 Issue:2 Volume:20 Page:618-640
ISSN:1540-3459
Container-title:Multiscale Modeling & Simulation
language:en
Short-container-title:Multiscale Model. Simul.

Author:

Wang Yating¹,Leung Wing Tat²,Lin Guang³

Affiliation:

1. Department of Mathematics, Purdue University, West Lafayette, IN 47907 USA, and Department of Mathematics, University of Hong Kong, Hong Kong SAR, China ().

2. Department of Mathematics, University of California, Irvine, Irvine, CA 92697 USA ().

3. Department of Mathematics, School of Mechanical Engineering, Department of Statistics (Courtesy), and Department of Earth, Atmospheric, and Planetary Sciences (Courtesy), Purdue University, West Lafayette, IN 47907 USA ().

Funder

National Science Foundation

Brookhaven National Laboratory

U.S. Department of Energy (DOE) Office of Science Advanced Scientific Computing Research

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Computer Science Applications,General Physics and Astronomy,Ecological Modeling,Modeling and Simulation,General Chemistry

Reference30 articles.

1. Mixed Multiscale Finite Element Methods Using Limited Global Information

2. Global–local nonlinear model reduction for flows in heterogeneous porous media

3. A Multiscale Mortar Mixed Finite Element Method

4. A prioriconvergence of the Greedy algorithm for the parametrized reduced basis method

5. Adaptive Mixed GMsFEM for Flows in Heterogeneous Media

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