Szczegóły publikacji
Opis bibliograficzny
Influence of activation functions on the convergence of physics-informed neural networks for 1D wave equation / Paweł MACZUGA, Maciej PASZYŃSKI // W: Computational Science – ICCS 2023 : 23rd International Conference : Prague, Czech Republic, July 3–5, 2023 : proceedings, Pt. 1 / eds. Jiří Mikyška [et al.]. — Cham : Springer Nature, cop. 2023. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 14073). — ISBN: 978-3-031-35994-1; e-ISBN: 978-3-031-35995-8. — S. 74–88. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-06-26
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147664 |
|---|---|
| Data dodania do BaDAP | 2023-07-21 |
| DOI | 10.1007/978-3-031-35995-8_6 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 23rd International Conference on Computational Science |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
In this paper, we consider a model wave equation. We perform a sequence of numerical experiments with Physics Informed Neural Network, considering different activation functions, and different ways of enforcing the initial and boundary conditions. We show the convergence of the method and the resulting numerical accuracy for different setups. We show that, indeed, the PINN methodology can solve the problem efficiently and accurately the wave-equations without actually solving a system of linear equations as it happens in traditional numerical methods like, e.g., finite element or finite difference method. In particular, we compare the influence of selected activation functions on the convergence of the PINN method. Our PINN code is available on github: https://github.com/pmaczuga/pinn-comparison/tree/iccs.