Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shi, Beiji"'
Autor:
Huang, Xiang, Ye, Zhanhong, Liu, Hongsheng, Shi, Beiji, Wang, Zidong, Yang, Kang, Li, Yang, Weng, Bingya, Wang, Min, Chu, Haotian, Yu, Fan, Hua, Bei, Chen, Lei, Dong, Bin
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc. Recently, build
Externí odkaz:
http://arxiv.org/abs/2111.08823
Autor:
Huang, Xiang, Liu, Hongsheng, Shi, Beiji, Wang, Zidong, Yang, Kang, Li, Yang, Weng, Bingya, Wang, Min, Chu, Haotian, Zhou, Jing, Yu, Fan, Hua, Bei, Chen, Lei, Dong, Bin
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems. PDEs
Externí odkaz:
http://arxiv.org/abs/2111.01394
Publikováno v:
In Aerospace Science and Technology March 2023 134
Akademický článek
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Autor:
Shi, Beiji1,2, Yang, Xiaolei1,3, Jin, Guodong1,2, He, Guowei1,2, Wang, Shizhao1,2 wangsz@lnm.imech.ac.cn
Publikováno v:
Applied Mathematics & Mechanics. Mar2019, Vol. 40 Issue 3, p305-320. 16p.
Publikováno v:
In Theoretical and Applied Mechanics Letters November 2016 6(6):302-305