Machine Learning Moment Closure Models for the Radiative Transfer Equation II: Enforcing Global Hyperbolicity in Gradient-Based Closures-Reference-Cited by-同舟云学术

Machine Learning Moment Closure Models for the Radiative Transfer Equation II: Enforcing Global Hyperbolicity in Gradient-Based Closures

Published:2023-04-25 Issue:2 Volume:21 Page:489-512
ISSN:1540-3459
Container-title:Multiscale Modeling & Simulation
language:en
Short-container-title:Multiscale Model. Simul.

Author:

Huang Juntao¹^ORCID,Cheng Yingda²,Christlieb Andrew J.³,Roberts Luke F.⁴,Yong Wen-An⁵^ORCID

Affiliation:

1. Department of Mathematics, Michigan State University, East Lansing, MI 48824 USA.

2. Department of Mathematics, Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI 48824 USA.

3. Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI 48824 USA.

4. National Superconducting Cyclotron Laboratory and Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 USA.

5. Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China; and Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.

Funder

National Science Foundation

Air Force Office of Scientific Research

National Natural Science Foundation of China

National Key Research and Development Program of China

U.S. Department of Energy

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

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

Reference44 articles.

1. Adaptive change of basis in entropy-based moment closures for linear kinetic equations

2. High-Order Entropy-Based Closures for Linear Transport in Slab Geometry II: A Computational Study of the Optimization Problem

3. Approximating the $M_2$ method by the extended quadrature method of moments for radiative transfer in slab geometry

4. A neural network closure for the Euler-Poisson system based on kinetic simulations

5. Discovering governing equations from data by sparse identification of nonlinear dynamical systems