127555 20230911114509.0 doi 10.1016/j.cma.2021.113763 sideral 123957 ART-2021-123957 eng Hernandez, Quercus Universidad de Zaragoza Deep learning of thermodynamics-aware reduced-order models from data 2021 We present an algorithm to learn the relevant latent variables of a large-scale discretized physical system and predict its time evolution using thermodynamically-consistent deep neural networks. Our method relies on sparse autoencoders, which reduce the dimensionality of the full order model to a set of sparse latent variables with no prior knowledge of the coded space dimensionality. Then, a second neural network is trained to learn the metriplectic structure of those reduced physical variables and predict its time evolution with a so-called structure-preserving neural network. This data-based integrator is guaranteed to conserve the total energy of the system and the entropy inequality, and can be applied to both conservative and dissipative systems. The integrated paths can then be decoded to the original full-dimensional manifold and be compared to the ground truth solution. This method is tested with two examples applied to fluid and solid mechanics. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MINECO-CICYT/DPI2017-85139-C2-1-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 6.588 2021 MECHANICS 9 / 138 = 0.065 2021 Q1 T1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 4 / 108 = 0.037 2021 Q1 T1 ENGINEERING, MULTIDISCIPLINARY 8 / 92 = 0.087 2021 Q1 T1 2.179 2021 Computational Mechanics 2021 Q1 Physics and Astronomy (miscellaneous) 2021 Q1 Mechanics of Materials 2021 Q1 Computer Science Applications 2021 Q1 10.3 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Badías, Alberto Universidad de Zaragoza (orcid)0000-0001-7639-6767 González, David Universidad de Zaragoza (orcid)0000-0003-3003-5856 Chinesta, Francisco Cueto, Elías Universidad de Zaragoza (orcid)0000-0003-1017-4381 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 379 (2021), 113763 [18 pp] Comput. methods appl. mech. eng. Computer Methods in Applied Mechanics and Engineering 0045-7825 1315073 http://zaguan.unizar.es/record/127555/files/texto_completo.pdf Versión publicada 1823554 http://zaguan.unizar.es/record/127555/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:127555 articulos driver 2023-09-11-10:59:30 ARTICLE