Publication:
Deep learned one‐iteration nonlinear solver for solid mechanics
Deep learned one‐iteration nonlinear solver for solid mechanics
| datacite.subject.fos | oecd::Natural sciences::Mathematics | |
| dc.contributor.author | Tan N. Nguyen | |
| dc.contributor.author | Jaehong Lee | |
| dc.contributor.author | Liem Dinh‐Tien | |
| dc.contributor.author | L. Minh Dang | |
| dc.date.accessioned | 2022-11-09T07:43:46Z | |
| dc.date.available | 2022-11-09T07:43:46Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The novel one-iteration nonlinear solver (OINS) using time series prediction and the modified Riks method (M-R) is proposed in this paper. OINS is established upon the core idea as follows: (1) Firstly, we predict the load factor increment and the displacement vector increment and the convergent solution of the considering load step via the predictive networks which are trained by using the load factor and the displacement vector increments of the previous convergence steps and group method of data handling (GMDH); (2) Thanks to the predicted convergence solution of the load step is very close to or identical with the real one, the prediction phase used in any existing nonlinear solvers is eliminated completely in OINS. | |
| dc.identifier.doi | 10.1002/nme.6918 | |
| dc.identifier.uri | http://repository.vlu.edu.vn:443/handle/123456789/1062 | |
| dc.language.iso | en_US | |
| dc.relation.ispartof | International Journal for Numerical Methods in Engineering | |
| dc.relation.issn | 0029-5981 | |
| dc.relation.issn | 1097-0207 | |
| dc.subject | Deep learning | |
| dc.subject | nonlinear | |
| dc.subject | one iteration | |
| dc.subject | solver | |
| dc.title | Deep learned one‐iteration nonlinear solver for solid mechanics | |
| dc.type | journal-article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 8 | |
| oaire.citation.volume | 123 |