Szczegóły publikacji
Opis bibliograficzny
Physics informed neural networks for non-stationary material science problems / Paweł MACZUGA, Tomasz SŁUŻALEC, Łukasz SZTANGRET, Danuta SZELIGA, Marcin ŁOŚ, Maciej PASZYŃSKI // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings , Pt. 2 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15908 ). — ISBN: 978-3-031-97556-1; e-ISBN: 978-3-031-97557-8. — S. 332–346. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-06
Autorzy (6)
Dane bibliometryczne
| ID BaDAP | 161048 |
|---|---|
| Data dodania do BaDAP | 2025-07-10 |
| DOI | 10.1007/978-3-031-97557-8_24 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Linear elasticity and Navier-Stokes equations are fundamental tools in material science, enabling the modeling of solid deformations and fluid flows under various conditions. These equations are widely used to simulate stresses, strains, and fluid interactions in processes like 3D printing, welding, casting, and extrusion. Physics-Informed Neural Networks (PINNs), introduced in 2019, have gained significant attention for solving complex physical problems, including fluid mechanics, wave propagation, and inverse problems. Despite their growing popularity, PINNs face challenges in training efficiency and accuracy. This paper investigates the applicability of modern PINN methodologies to material science problems involving Navier-Stokes and linear elasticity equations. For linear elasticity, a randomized selection of collocation points is employed to enhance training. For Navier-Stokes equations, hard constraints on initial and boundary conditions are implemented to avoid multi-objective optimization. These approaches aim to address training difficulties and improve PINN performance in simulating material science phenomena.
