000145388 001__ 145388
000145388 005__ 20241024135331.0
000145388 0247_ $$2doi$$a10.1007/s00466-024-02564-3
000145388 0248_ $$2sideral$$a140215
000145388 037__ $$aART-2024-140215
000145388 041__ $$aeng
000145388 100__ $$0(orcid)0000-0001-6727-563X$$aUrdeitx, Pau$$uUniversidad de Zaragoza
000145388 245__ $$aA comparison of single and double generator formalisms for thermodynamics-informed neural networks
000145388 260__ $$c2024
000145388 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145388 5203_ $$aThe 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.
000145388 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MTFP/TSI-100930-2023-1
000145388 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145388 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145388 700__ $$0(orcid)0000-0002-9135-866X$$aAlfaro, Icíar$$uUniversidad de Zaragoza
000145388 700__ $$0(orcid)0000-0003-3003-5856$$aGonzález, David$$uUniversidad de Zaragoza
000145388 700__ $$aChinesta, Francisco
000145388 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000145388 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000145388 773__ $$g(2024), [17 pp.]$$pComput. mech.$$tCOMPUTATIONAL MECHANICS$$x0178-7675
000145388 8564_ $$s1243764$$uhttps://zaguan.unizar.es/record/145388/files/texto_completo.pdf$$yVersión publicada
000145388 8564_ $$s2000858$$uhttps://zaguan.unizar.es/record/145388/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145388 909CO $$ooai:zaguan.unizar.es:145388$$particulos$$pdriver
000145388 951__ $$a2024-10-24-12:11:53
000145388 980__ $$aARTICLE