Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology

Fatemeh Chegini; Alena Kopaničáková; Martin Weiser; Rolf Krause

doi:10.1007/978-3-030-95025-5_6

Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology

Fatemeh Chegini^*, Alena Kopaničáková, Martin Weiser, Rolf Krause

^*Corresponding author for this work

Applied Mathematics and Computational Science

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

Abstract

Reliable cardiac excitation predictions depend not only on accurate geometric and physiological models, usually formulated as PDEs, and our ability to solve those faithfully, but also on the model’s correct parameterization.

Original language	English (US)
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	67-78
Number of pages	12
ISBN (Print)	9783030950248
DOIs	https://doi.org/10.1007/978-3-030-95025-5_6
State	Published - 2022
Event	26th International Conference on Domain Decomposition Methods, 2020 - Virtual, Online Duration: Dec 7 2020 → Dec 12 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	12/7/20 → 12/12/20

Bibliographical note

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

ASJC Scopus subject areas

Modeling and Simulation
General Engineering
Discrete Mathematics and Combinatorics
Control and Optimization
Computational Mathematics

Access to Document

10.1007/978-3-030-95025-5_6

Cite this

Chegini, F., Kopaničáková, A., Weiser, M., & Krause, R. (2022). Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology. In S. C. Brenner, A. Klawonn, J. Xu, E. Chung, J. Zou, & F. Kwok (Eds.), Domain Decomposition Methods in Science and Engineering XXVI (pp. 67-78). (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_6

Chegini, Fatemeh ; Kopaničáková, Alena ; Weiser, Martin et al. / Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology. 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. 67-78 (Lecture Notes in Computational Science and Engineering).

@inproceedings{64d60df1a4454418b6554c3d744c39a6,

title = "Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology",

abstract = "Reliable cardiac excitation predictions depend not only on accurate geometric and physiological models, usually formulated as PDEs, and our ability to solve those faithfully, but also on the model{\textquoteright}s correct parameterization.",

author = "Fatemeh Chegini and Alena Kopani{\v c}{\'a}kov{\'a} and Martin Weiser and Rolf Krause",

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_6",

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isbn = "9783030950248",

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

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

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address = "Germany",

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Chegini, F, Kopaničáková, A, Weiser, M & Krause, R 2022, Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology. 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. 67-78, 26th International Conference on Domain Decomposition Methods, 2020, Virtual, Online, 12/7/20. https://doi.org/10.1007/978-3-030-95025-5_6

Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology. / Chegini, Fatemeh; Kopaničáková, Alena; Weiser, Martin et al.
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. 67-78 (Lecture Notes in Computational Science and Engineering; Vol. 145).

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

TY - GEN

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AU - Chegini, Fatemeh

AU - Kopaničáková, Alena

AU - Weiser, Martin

AU - Krause, Rolf

PY - 2022

Y1 - 2022

N2 - Reliable cardiac excitation predictions depend not only on accurate geometric and physiological models, usually formulated as PDEs, and our ability to solve those faithfully, but also on the model’s correct parameterization.

AB - Reliable cardiac excitation predictions depend not only on accurate geometric and physiological models, usually formulated as PDEs, and our ability to solve those faithfully, but also on the model’s correct parameterization.

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DO - 10.1007/978-3-030-95025-5_6

M3 - Conference contribution

AN - SCOPUS:85151156378

SN - 9783030950248

T3 - Lecture Notes in Computational Science and Engineering

SP - 67

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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

ER -

Chegini F, Kopaničáková A, Weiser M, Krause R. Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology. 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. 67-78. (Lecture Notes in Computational Science and Engineering). doi: 10.1007/978-3-030-95025-5_6

Quantitative Analysis of Nonlinear Multifidelity Optimization for Inverse Electrophysiology

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