dc.contributor.author | Uriarte Baranda, Carlos | |
dc.contributor.author | Pardo Zubiaur, David | |
dc.contributor.author | Omella Milian, Ángel Javier | |
dc.date.accessioned | 2024-05-22T14:41:58Z | |
dc.date.available | 2024-05-22T14:41:58Z | |
dc.date.issued | 2022-03 | |
dc.identifier.citation | Computer Methods in Applied Mechanics and Engineering 391 : (2022) // Article ID 114562 | es_ES |
dc.identifier.issn | 1879-2138 | |
dc.identifier.issn | 0045-7825 | |
dc.identifier.uri | http://hdl.handle.net/10810/68101 | |
dc.description.abstract | We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations (PDEs). The connections between neurons in the architecture mimic the Finite Element connectivity graph when applying mesh refinements. We select and discuss several losses employing preconditioners and different norms to enhance convergence. For simplicity, we implement the resulting Deep-FEM in one spatial domain (1D), although its extension to 2D and 3D problems is straightforward. Extensive numerical experiments show in general good approximations for both symmetric positive definite (SPD) and indefinite problems in parametric and non-parametric problems. However, in some cases, lack of convexity prevents us from obtaining high-accuracy solutions. | es_ES |
dc.description.sponsorship | This work has received funding from: the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 777778 (MATHROCKS); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014–2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation projects with references PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00, the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018–2021 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/777778 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2019-108111RB-I00 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PDC2021-121093-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | deep learning | es_ES |
dc.subject | neural networks | es_ES |
dc.subject | partial differential equations | es_ES |
dc.subject | finite element method | es_ES |
dc.title | A Finite Element based Deep Learning solver for parametric PDEs | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:
//creativecommons.org/licenses/by-nc-nd/4.0/) | es_ES |
dc.rights.holder | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0045782521007374 | es_ES |
dc.identifier.doi | 10.1016/j.cma.2021.114562 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Matemáticas | es_ES |
dc.departamentoeu | Matematika | es_ES |