A Scalable Deep Learning Approach for Solving High-Dimensional Dynamic Optimal Transport-Reference-Cited by-同舟云学术

A Scalable Deep Learning Approach for Solving High-Dimensional Dynamic Optimal Transport

Published:2023-08-01 Issue:4 Volume:45 Page:B544-B563
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Wan Wei¹,Zhang Yuejin²,Bao Chenglong³,Dong Bin⁴,Shi Zuoqiang³^ORCID

Affiliation:

1. School of Mathematics and Physics, North China Electric Power University, Beijing, China.

2. Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China.

3. Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China, and Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.

4. Beijing International Center for Mathematical Research and Center for Machine Learning Research, Peking University, Beijing, China.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Tsinghua University Initiative Scientific Research Program

Beijing Natural Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Computational Mathematics

Reference31 articles.

1. Convolutional Neural Networks for Speech Recognition

2. A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem

3. Numerical solution of the Optimal Transportation problem using the Monge–Ampère equation

4. Polar factorization and monotone rearrangement of vector-valued functions

5. Optimal Transport for Domain Adaptation

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