A pre-training deep learning method for simulating the large bending deformation of bilayer plates
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916321280655360 |
|---|---|
| author | Li, Xiang Liao, Yulei Ming, Pingbing |
| author_facet | Li, Xiang Liao, Yulei Ming, Pingbing |
| contents | We propose a deep learning based method for simulating the large bending deformation of bilayer plates. Inspired by the greedy algorithm, we propose a pre-training method on a series of nested domains, which accelerate the convergence of training and find the absolute minimizer more effectively. The proposed method exhibits the capability to converge to an absolute minimizer, overcoming the limitation of gradient flow methods getting trapped in the local minimizer basins. We showcase better performance with fewer numbers of degrees of freedom for the relative energy errors and relative $L^2$-errors of the minimizer through numerical experiments. Furthermore, our method successfully maintains the $L^2$-norm of the isometric constraint, leading to an improvement of accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_04967 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A pre-training deep learning method for simulating the large bending deformation of bilayer plates Li, Xiang Liao, Yulei Ming, Pingbing Numerical Analysis 39-08, 49S05, 65K10, 65Q20, 74-10, 74K20, 74G65 We propose a deep learning based method for simulating the large bending deformation of bilayer plates. Inspired by the greedy algorithm, we propose a pre-training method on a series of nested domains, which accelerate the convergence of training and find the absolute minimizer more effectively. The proposed method exhibits the capability to converge to an absolute minimizer, overcoming the limitation of gradient flow methods getting trapped in the local minimizer basins. We showcase better performance with fewer numbers of degrees of freedom for the relative energy errors and relative $L^2$-errors of the minimizer through numerical experiments. Furthermore, our method successfully maintains the $L^2$-norm of the isometric constraint, leading to an improvement of accuracy. |
| title | A pre-training deep learning method for simulating the large bending deformation of bilayer plates |
| topic | Numerical Analysis 39-08, 49S05, 65K10, 65Q20, 74-10, 74K20, 74G65 |
| url | https://arxiv.org/abs/2308.04967 |