A multigrid and neural network approach to reduce the computational cost of phi-FEM
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917492141588480 |
|---|---|
| author | Bulle, Raphaël Duprez, Michel Lleras, Vanessa Vuillemot, Killian |
| author_facet | Bulle, Raphaël Duprez, Michel Lleras, Vanessa Vuillemot, Killian |
| contents | In this work, we present a combination of a multigrid approach and the phi-FEM immersed boundary finite element method to reduce its computational cost while preserving its accuracy. To further reduce the numerical cost of the approach, we also propose the combination of the previous technique with some neural network methods. We illustrate the efficiency of these two approaches with numerical test cases in 2D and 3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13718 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A multigrid and neural network approach to reduce the computational cost of phi-FEM Bulle, Raphaël Duprez, Michel Lleras, Vanessa Vuillemot, Killian Numerical Analysis 65N30, 65N85, 65N15 In this work, we present a combination of a multigrid approach and the phi-FEM immersed boundary finite element method to reduce its computational cost while preserving its accuracy. To further reduce the numerical cost of the approach, we also propose the combination of the previous technique with some neural network methods. We illustrate the efficiency of these two approaches with numerical test cases in 2D and 3D. |
| title | A multigrid and neural network approach to reduce the computational cost of phi-FEM |
| topic | Numerical Analysis 65N30, 65N85, 65N15 |
| url | https://arxiv.org/abs/2605.13718 |