Domain Decomposition Algorithms for Physics-Informed Neural Networks

Hyea Hyun Kim; Hee Jun Yang

doi:10.1007/978-3-030-95025-5_76

Domain Decomposition Algorithms for Physics-Informed Neural Networks

Hyea Hyun Kim
, Hee Jun Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Domain decomposition algorithms are widely used as fast solutions of algebraic equations arising from the discretization of partial differential equations. The original algebraic equations are partitioned and solved in each subdomain combined with an iterative procedure.

Original language	English
Title of host publication	Domain Decomposition Methods in Science and Engineering XXVI
Editors	Susanne C. Brenner, Axel Klawonn, Jinchao Xu, Eric Chung, Jun Zou, Felix Kwok
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	697-704
Number of pages	8
ISBN (Print)	9783030950248
DOIs	https://doi.org/10.1007/978-3-030-95025-5_76
Publication status	Published - 2022
Event	26th International Conference on Domain Decomposition Methods, 2020 - Virtual, Online Duration: 7 Dec 2020 → 12 Dec 2020

Publication series

Name	Lecture Notes in Computational Science and Engineering
Volume	145
ISSN (Print)	1439-7358
ISSN (Electronic)	2197-7100

Conference

Conference	26th International Conference on Domain Decomposition Methods, 2020
City	Virtual, Online
Period	7/12/20 → 12/12/20

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-030-95025-5_76

Cite this

Kim, H. H., & Yang, H. J. (2022). Domain Decomposition Algorithms for Physics-Informed Neural Networks. In S. C. Brenner, A. Klawonn, J. Xu, E. Chung, J. Zou, & F. Kwok (Eds.), Domain Decomposition Methods in Science and Engineering XXVI (pp. 697-704). (Lecture Notes in Computational Science and Engineering; Vol. 145). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-95025-5_76

Kim, Hyea Hyun ; Yang, Hee Jun. / Domain Decomposition Algorithms for Physics-Informed Neural Networks. Domain Decomposition Methods in Science and Engineering XXVI. editor / Susanne C. Brenner ; Axel Klawonn ; Jinchao Xu ; Eric Chung ; Jun Zou ; Felix Kwok. Springer Science and Business Media Deutschland GmbH, 2022. pp. 697-704 (Lecture Notes in Computational Science and Engineering).

@inproceedings{40814ebf1ab2469f8205d69d6dfa4fdf,

title = "Domain Decomposition Algorithms for Physics-Informed Neural Networks",

abstract = "Domain decomposition algorithms are widely used as fast solutions of algebraic equations arising from the discretization of partial differential equations. The original algebraic equations are partitioned and solved in each subdomain combined with an iterative procedure.",

author = "Kim, \{Hyea Hyun\} and Yang, \{Hee Jun\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 26th International Conference on Domain Decomposition Methods, 2020 ; Conference date: 07-12-2020 Through 12-12-2020",

year = "2022",

doi = "10.1007/978-3-030-95025-5\_76",

language = "English",

isbn = "9783030950248",

series = "Lecture Notes in Computational Science and Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "697--704",

editor = "Brenner, \{Susanne C.\} and Axel Klawonn and Jinchao Xu and Eric Chung and Jun Zou and Felix Kwok",

booktitle = "Domain Decomposition Methods in Science and Engineering XXVI",

address = "Germany",

}

Kim, HH & Yang, HJ 2022, Domain Decomposition Algorithms for Physics-Informed Neural Networks. in SC Brenner, A Klawonn, J Xu, E Chung, J Zou & F Kwok (eds), Domain Decomposition Methods in Science and Engineering XXVI. Lecture Notes in Computational Science and Engineering, vol. 145, Springer Science and Business Media Deutschland GmbH, pp. 697-704, 26th International Conference on Domain Decomposition Methods, 2020, Virtual, Online, 7/12/20. https://doi.org/10.1007/978-3-030-95025-5_76

Domain Decomposition Algorithms for Physics-Informed Neural Networks. / Kim, Hyea Hyun; Yang, Hee Jun.
Domain Decomposition Methods in Science and Engineering XXVI. ed. / Susanne C. Brenner; Axel Klawonn; Jinchao Xu; Eric Chung; Jun Zou; Felix Kwok. Springer Science and Business Media Deutschland GmbH, 2022. p. 697-704 (Lecture Notes in Computational Science and Engineering; Vol. 145).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kim, Hyea Hyun

AU - Yang, Hee Jun

PY - 2022

Y1 - 2022

N2 - Domain decomposition algorithms are widely used as fast solutions of algebraic equations arising from the discretization of partial differential equations. The original algebraic equations are partitioned and solved in each subdomain combined with an iterative procedure.

AB - Domain decomposition algorithms are widely used as fast solutions of algebraic equations arising from the discretization of partial differential equations. The original algebraic equations are partitioned and solved in each subdomain combined with an iterative procedure.

UR - https://www.scopus.com/pages/publications/85151146305

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DO - 10.1007/978-3-030-95025-5_76

M3 - Conference contribution

AN - SCOPUS:85151146305

SN - 9783030950248

T3 - Lecture Notes in Computational Science and Engineering

SP - 697

EP - 704

BT - Domain Decomposition Methods in Science and Engineering XXVI

A2 - Brenner, Susanne C.

A2 - Klawonn, Axel

A2 - Xu, Jinchao

A2 - Chung, Eric

A2 - Zou, Jun

A2 - Kwok, Felix

PB - Springer Science and Business Media Deutschland GmbH

T2 - 26th International Conference on Domain Decomposition Methods, 2020

Y2 - 7 December 2020 through 12 December 2020

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Kim HH, Yang HJ. Domain Decomposition Algorithms for Physics-Informed Neural Networks. In Brenner SC, Klawonn A, Xu J, Chung E, Zou J, Kwok F, editors, Domain Decomposition Methods in Science and Engineering XXVI. Springer Science and Business Media Deutschland GmbH. 2022. p. 697-704. (Lecture Notes in Computational Science and Engineering). doi: 10.1007/978-3-030-95025-5_76

Domain Decomposition Algorithms for Physics-Informed Neural Networks

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