145388 20260217205531.0 doi 10.1007/s00466-024-02564-3 sideral 140215 ART-2024-140215 eng Urdeitx, Pau Universidad de Zaragoza (orcid)0000-0001-6727-563X A comparison of single and double generator formalisms for thermodynamics-informed neural networks 2024 Access copy available to the general public Unrestricted The development of inductive biases has been shown to be a very effective way to increase the accuracy and robustness of neural networks, particularly when they are used to predict physical phenomena. These biases significantly increase the certainty of predictions, decrease the error made and allow considerably smaller datasets to be used. There are a multitude of methods in the literature to develop these biases. One of the most effective ways, when dealing with physical phenomena, is to introduce physical principles of recognised validity into the network architecture. The problem becomes more complex without knowledge of the physical principles governing the phenomena under study. A very interesting possibility then is to turn to the principles of thermodynamics, which are universally valid, regardless of the level of abstraction of the description sought for the phenomenon under study. To ensure compliance with the principles of thermodynamics, there are formulations that have a long tradition in many branches of science. In the field of rheology, for example, two main types of formalisms are used to ensure compliance with these principles: one-generator and two-generator formalisms. In this paper we study the advantages and disadvantages of each, using classical problems with known solutions and synthetic data. info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033 info:eu-repo/grantAgreement/ES/MTFP/TSI-100930-2023-1 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.8 2024 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 16 / 136 = 0.118 2024 Q1 T1 MECHANICS 36 / 171 = 0.211 2024 Q1 T1 1.256 2024 Applied Mathematics 2024 Q1 Computational Mathematics 2024 Q1 Ocean Engineering 2024 Q1 Computational Theory and Mathematics 2024 Q1 Mechanical Engineering 2024 Q1 Computational Mechanics 2024 Q1 7.9 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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. (2024), [17 pp.] Comput. mech. COMPUTATIONAL MECHANICS 0178-7675 2935882 http://zaguan.unizar.es/record/145388/files/texto_completo.pdf Versión publicada 2248813 http://zaguan.unizar.es/record/145388/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:145388 articulos driver 2026-02-17-20:32:40 ARTICLE