CSpace
Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
Liao, Yulei1,2; Ming, Pingbing1,2
2021-05-01
发表期刊COMMUNICATIONS IN COMPUTATIONAL PHYSICS
ISSN1815-2406
卷号29期号:5页码:1365-1384
摘要We propose a new method to deal with the essential boundary conditions encountered in the deep learning-based numerical solvers for partial differential equations. The trial functions representing by deep neural networks are non-interpolatory, which makes the enforcement of the essential boundary conditions a nontrivial matter. Our method resorts to Nitsche?s variational formulation to deal with this difficulty, which is consistent, and does not require significant extra computational costs. We prove the error estimate in the energy norm and illustrate the method on several representative problems posed in at most 100 dimension.
关键词Deep Nitsche Method Deep Ritz Method neural network approximation mixed boundary conditions curse of dimensionality
DOI10.4208/cicp.OA-2020-0219
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[11971467] ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向Physics
WOS类目Physics, Mathematical
WOS记录号WOS:000633053700003
出版者GLOBAL SCIENCE PRESS
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/58406
专题中国科学院数学与系统科学研究院
通讯作者Ming, Pingbing
作者单位1.Chinese Acad Sci, AMSS, Inst Computat Math & Sci Engn Comp, LSEC, 55 East Rd Zhong Guan Cun, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Liao, Yulei,Ming, Pingbing. Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions[J]. COMMUNICATIONS IN COMPUTATIONAL PHYSICS,2021,29(5):1365-1384.
APA Liao, Yulei,&Ming, Pingbing.(2021).Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions.COMMUNICATIONS IN COMPUTATIONAL PHYSICS,29(5),1365-1384.
MLA Liao, Yulei,et al."Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions".COMMUNICATIONS IN COMPUTATIONAL PHYSICS 29.5(2021):1365-1384.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Liao, Yulei]的文章
[Ming, Pingbing]的文章
百度学术
百度学术中相似的文章
[Liao, Yulei]的文章
[Ming, Pingbing]的文章
必应学术
必应学术中相似的文章
[Liao, Yulei]的文章
[Ming, Pingbing]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。