136321 20260217205531.0 doi 10.1016/j.cma.2024.117210 sideral 139210 ART-2024-139210 eng Bermejo-Barbanoj, Carlos Universidad de Zaragoza Thermodynamics-informed super-resolution of scarce temporal dynamics data 2024 Access copy available to the general public Unrestricted We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of the full order model to a set of latent variables that are enforced to match a prior, for example a normal distribution. Adversarial autoencoders are seen as generative models, and they can be trained to generate high-resolution samples from low-resolution inputs, meaning they can address the so-called super-resolution problem. Then, a second neural network is trained to learn the physical structure of the latent variables and predict their temporal evolution. This neural network is known as a structure-preserving neural network. It learns the metriplectic-structure of the system and applies a physical bias to ensure that the first and second principles of thermodynamics are fulfilled. The integrated trajectories are decoded to their original dimensionality, as well as to the higher dimensionality space produced by the adversarial autoencoder and they are compared to the ground truth solution. The method is tested with two examples of flow over a cylinder, where the fluid properties are varied between both examples. 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 by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 7.3 2024 MECHANICS 7 / 171 = 0.041 2024 Q1 T1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 4 / 136 = 0.029 2024 Q1 T1 ENGINEERING, MULTIDISCIPLINARY 8 / 179 = 0.045 2024 Q1 T1 2.412 2024 Computational Mechanics 2024 Q1 Computer Science Applications 2024 Q1 Physics and Astronomy (miscellaneous) 2024 Q1 Mechanics of Materials 2024 Q1 Mechanical Engineering 2024 Q1 12.8 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Moya, Beatriz Badías, Alberto 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. 430 (2024), 117210 [16 pp.] Comput. methods appl. mech. eng. Computer Methods in Applied Mechanics and Engineering 0045-7825 3084182 http://zaguan.unizar.es/record/136321/files/texto_completo.pdf Versión publicada 1988447 http://zaguan.unizar.es/record/136321/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:136321 articulos driver 2026-02-17-20:32:33 ARTICLE