Szczegóły publikacji
Opis bibliograficzny
Petrov-Galerkin formulation equivallent to the residual minimization method for fiding an optimal test function / Maciej R. PASZYŃSKI, Tomasz D. Śluzalec // W: ECCOMAS congress 2022 [Dokument elektroniczny] : 8th European Congress on Computational Methods in Applied Sciences and Engineering : 5–9 June 2022, Oslo, Norway. — Wersja do Windows. — Dane tekstowe. — [Oslo : Norwegian University of Science and Technology], [2022]. — S. 1–12. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.scipedia.com/public/Paszynski_et_al_2022a [2022-12-06]. — Bibliogr. s. 11–12, Abstr. — Publikacja dostępna online od: 2022-11-24
Autorzy (2)
- AGHPaszyński Maciej
- Śluzalec Tomasz D.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 144050 |
|---|---|
| Data dodania do BaDAP | 2022-12-19 |
| DOI | 10.23967/eccomas.2022.079 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons |
Abstract
Numerical solutions of Partial Differential Equations with Finite Element Method have multiple applications in science and engineering. Several challenging problems require special stabilization methods to deliver accurate results of the numerical simulations. The advection-dominated diffusion problem is an example of such problems. They are employed to model pollution propagation in the atmosphere. Unstable numerical methods generate unphysical oscillations, and they make no physical sense. Obtaining accurate and stable numerical simulations is difficult, and the method of stabilization depends on the parameters of the partial differential equations. They require a deep knowledge of an expert in the field of numerical analysis. We propose a method to construct and train an artificial expert in stabilizing numerical simulations based on partial differential equations. We create a neural network-driven artificial intelligence that makes decisions about the method of stabilizing computer simulations. It will automatically stabilize difficult numerical simulations in a linear computational cost by generating the optimal test functions. These test functions can be utilized for building an unconditionally stable system of linear equations. The optimal test functions proposed by artificial intelligence will not depend on the right-hand side, and thus they may be utilized in a large class of PDE-based simulations with different forcing and boundary conditions. We test our method on the model one-dimensional advection-dominated diffusion problem.