150776 20260116150736.0 doi 10.1016/j.engappai.2025.110108 sideral 142872 ART-2025-142872 eng Tierz, Alicia Universidad de Zaragoza (orcid)0009-0007-8488-6089 Graph neural networks informed locally by thermodynamics 2025 Access copy available to the general public Unrestricted Thermodynamics-informed neural networks employ inductive biases for the enforcement of the first and second principles of thermodynamics. To construct these biases, a metriplectic evolution of the physical system under study is assumed. This provides excellent results, when compared to uninformed, black box networks. While the degree of accuracy can be increased in one or two orders of magnitude, in the case of graph networks, this requires assembling global Poisson and dissipation matrices, which breaks the local structure of such networks. In order to avoid this drawback, a local version of the metriplectic biases has been developed in this work, which avoids the aforementioned matrix assembly, thus preserving the node-by-node structure of the graph networks. We apply this framework for examples in the fields of solid and fluid mechanics. Our approach demonstrates significant computational efficiency and strong generalization capabilities, accurately making inferences on examples significantly different from those encountered during training. info:eu-repo/grantAgreement/EUR/MICINN/TED2021- 130105B-I00 info:eu-repo/grantAgreement/ES/MTFP/TSI-100930-2023-1 info:eu-repo/semantics/embargoedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion Alfaro, Icíar Universidad de Zaragoza (orcid)0000-0002-9135-866X 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. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 144 (2025), 110108 [12 pp.] Eng. appl. artif. intell. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 0952-1976 4223908 http://zaguan.unizar.es/record/150776/files/texto_completo.pdf Preprint info:eu-repo/date/embargoEnd/2027-01-27 2136740 http://zaguan.unizar.es/record/150776/files/texto_completo.jpg?subformat=icon icon Preprint info:eu-repo/date/embargoEnd/2027-01-27 oai:zaguan.unizar.es:150776 articulos driver 2026-01-16-15:06:13 ARTICLE