Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method

Victorita Dolean; Alexander Heinlein; Siddhartha Mishra; Ben Moseley

doi:10.1007/978-3-031-50769-4_19

Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method

Victorita Dolean^*
, Alexander Heinlein
, Siddhartha Mishra
, Ben Moseley

^*Corresponding author for this work

Numerical Analysis

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

27 Downloads (Pure)

Abstract

The success and advancement of machine learning (ML) in fields such as image recognition and natural language processing has lead to the development of novel methods for the solution of problems in physics and engineering.

Original language	English
Title of host publication	Domain Decomposition Methods in Science and Engineering XXVII
Editors	Zdenek Dostal, Tomas Kozubek, Axel Klawonn, Luca F. Pavarino, Olof B. Widlund, Ulrich Langer, Jakub Sístek
Publisher	Springer
Pages	165-172
Number of pages	8
ISBN (Print)	9783031507687
DOIs	https://doi.org/10.1007/978-3-031-50769-4_19
Publication status	Published - 2024
Event	27th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2022 - Prague, Czechia Duration: 25 Jul 2022 → 29 Jul 2022

Publication series

Name	Lecture Notes in Computational Science and Engineering
Volume	149
ISSN (Print)	1439-7358
ISSN (Electronic)	2197-7100

Conference

Conference	27th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2022
Country/Territory	Czechia
City	Prague
Period	25/07/22 → 29/07/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Access to Document

10.1007/978-3-031-50769-4_19

978-3-031-50769-4_19Final published version, 501 KB

Cite this

Dolean, V., Heinlein, A., Mishra, S., & Moseley, B. (2024). Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method. In Z. Dostal, T. Kozubek, A. Klawonn, L. F. Pavarino, O. B. Widlund, U. Langer, & J. Sístek (Eds.), Domain Decomposition Methods in Science and Engineering XXVII (pp. 165-172). (Lecture Notes in Computational Science and Engineering; Vol. 149). Springer. https://doi.org/10.1007/978-3-031-50769-4_19

Dolean, Victorita ; Heinlein, Alexander ; Mishra, Siddhartha et al. / Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method. Domain Decomposition Methods in Science and Engineering XXVII. editor / Zdenek Dostal ; Tomas Kozubek ; Axel Klawonn ; Luca F. Pavarino ; Olof B. Widlund ; Ulrich Langer ; Jakub Sístek. Springer, 2024. pp. 165-172 (Lecture Notes in Computational Science and Engineering).

@inproceedings{8f82b2faf0d248dfb2682695f68b988c,

title = "Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method",

abstract = "The success and advancement of machine learning (ML) in fields such as image recognition and natural language processing has lead to the development of novel methods for the solution of problems in physics and engineering.",

author = "Victorita Dolean and Alexander Heinlein and Siddhartha Mishra and Ben Moseley",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 27th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2022 ; Conference date: 25-07-2022 Through 29-07-2022",

year = "2024",

doi = "10.1007/978-3-031-50769-4\_19",

language = "English",

isbn = "9783031507687",

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

publisher = "Springer",

pages = "165--172",

editor = "Zdenek Dostal and Tomas Kozubek and Axel Klawonn and Pavarino, \{Luca F.\} and Widlund, \{Olof B.\} and Ulrich Langer and Jakub S{\'i}stek",

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

}

Dolean, V, Heinlein, A, Mishra, S & Moseley, B 2024, Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method. in Z Dostal, T Kozubek, A Klawonn, LF Pavarino, OB Widlund, U Langer & J Sístek (eds), Domain Decomposition Methods in Science and Engineering XXVII. Lecture Notes in Computational Science and Engineering, vol. 149, Springer, pp. 165-172, 27th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2022, Prague, Czechia, 25/07/22. https://doi.org/10.1007/978-3-031-50769-4_19

Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method. / Dolean, Victorita; Heinlein, Alexander; Mishra, Siddhartha et al.
Domain Decomposition Methods in Science and Engineering XXVII. ed. / Zdenek Dostal; Tomas Kozubek; Axel Klawonn; Luca F. Pavarino; Olof B. Widlund; Ulrich Langer; Jakub Sístek. Springer, 2024. p. 165-172 (Lecture Notes in Computational Science and Engineering; Vol. 149).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method

AU - Dolean, Victorita

AU - Heinlein, Alexander

AU - Mishra, Siddhartha

AU - Moseley, Ben

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2024

Y1 - 2024

N2 - The success and advancement of machine learning (ML) in fields such as image recognition and natural language processing has lead to the development of novel methods for the solution of problems in physics and engineering.

AB - The success and advancement of machine learning (ML) in fields such as image recognition and natural language processing has lead to the development of novel methods for the solution of problems in physics and engineering.

UR - https://www.scopus.com/pages/publications/85184285748

U2 - 10.1007/978-3-031-50769-4_19

DO - 10.1007/978-3-031-50769-4_19

M3 - Conference contribution

AN - SCOPUS:85184285748

SN - 9783031507687

T3 - Lecture Notes in Computational Science and Engineering

SP - 165

EP - 172

BT - Domain Decomposition Methods in Science and Engineering XXVII

A2 - Dostal, Zdenek

A2 - Kozubek, Tomas

A2 - Klawonn, Axel

A2 - Pavarino, Luca F.

A2 - Widlund, Olof B.

A2 - Langer, Ulrich

A2 - Sístek, Jakub

PB - Springer

T2 - 27th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2022

Y2 - 25 July 2022 through 29 July 2022

ER -

Dolean V, Heinlein A, Mishra S, Moseley B. Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method. In Dostal Z, Kozubek T, Klawonn A, Pavarino LF, Widlund OB, Langer U, Sístek J, editors, Domain Decomposition Methods in Science and Engineering XXVII. Springer. 2024. p. 165-172. (Lecture Notes in Computational Science and Engineering). doi: 10.1007/978-3-031-50769-4_19

Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this