Enhancing Training of Physics-Informed Neural Networks Using Domain Decomposition–Based Preconditioning Strategies-Reference-Cited by-同舟云学术

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Enhancing Training of Physics-Informed Neural Networks Using Domain Decomposition–Based Preconditioning Strategies

Published:2024-04-11 Issue: Volume: Page:S46-S67
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Kopaničáková Alena¹^ORCID,Kothari Hardik²^ORCID,Karniadakis George E.³^ORCID,Krause Rolf⁴

Affiliation:

1. Euler Institute, Università della Svizzera italiana, Lugano, Switzerland, and Division of Applied Mathematics, Brown University, Providence, RI 02906 USA.

2. Euler Institute, Università della Svizzera italiana, Lugano, Switzerland.

3. Division of Applied Mathematics, Brown University, Providence, RI 02906 USA.

4. Euler Institute, Università della Svizzera italiana, Lugano, Switzerland, and UniDistance, Brig-Glis, Switzerland.

Funder

Swiss National Science Foundation

Platform for Advanced Scientific Computing

Vannevar Bush Faculty Fellowship

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference61 articles.

1. Demystifying Parallel and Distributed Deep Learning

2. Composing Scalable Nonlinear Algebraic Solvers

3. Representations of quasi-Newton matrices and their use in limited memory methods

4. Nonlinearly Preconditioned Inexact Newton Algorithms

5. Using Random forest and Gradient boosting trees to improve wave forecast at a specific location

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