Coupling PIES and PINN for Solving Two-Dimensional Boundary Value Problems via Domain Decomposition
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14834 LNCS, Page: 87-94
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The paper proposes coupling Parametric Integral Equation System (PIES) and Physics-Informed Neural Network (PINN) for solving two-dimensional potential boundary value problems defined by the Laplace equation. As a result, the computational domain can be decomposed into subdomains, where solutions are obtained independently using PIES and PINN while simultaneously satisfying interface connection conditions. The efficacy of this approach is validated through a numerical example.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85199635931&origin=inward; http://dx.doi.org/10.1007/978-3-031-63759-9_11; https://link.springer.com/10.1007/978-3-031-63759-9_11; https://dx.doi.org/10.1007/978-3-031-63759-9_11; https://link.springer.com/chapter/10.1007/978-3-031-63759-9_11
Springer Science and Business Media LLC
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