000136321 001__ 136321
000136321 005__ 20240731105612.0
000136321 0247_ $$2doi$$a10.1016/j.cma.2024.117210
000136321 0248_ $$2sideral$$a139210
000136321 037__ $$aART-2024-139210
000136321 041__ $$aeng
000136321 100__ $$aBermejo-Barbanoj, Carlos$$uUniversidad de Zaragoza
000136321 245__ $$aThermodynamics-informed super-resolution of scarce temporal dynamics data
000136321 260__ $$c2024
000136321 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136321 5203_ $$aWe present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of the full order model to a set of latent variables that are enforced to match a prior, for example a normal distribution. Adversarial autoencoders are seen as generative models, and they can be trained to generate high-resolution samples from low-resolution inputs, meaning they can address the so-called super-resolution problem.
Then, a second neural network is trained to learn the physical structure of the latent variables and predict their temporal evolution. This neural network is known as a structure-preserving neural network. It learns the metriplectic-structure of the system and applies a physical bias to ensure that the first and second principles of thermodynamics are fulfilled.
The integrated trajectories are decoded to their original dimensionality, as well as to the higher dimensionality space produced by the adversarial autoencoder and they are compared to the ground truth solution. The method is tested with two examples of flow over a cylinder, where the fluid properties are varied between both examples.
000136321 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MTFP/TSI-100930-2023-1
000136321 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000136321 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000136321 700__ $$aMoya, Beatriz
000136321 700__ $$aBadías, Alberto
000136321 700__ $$aChinesta, Francisco
000136321 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000136321 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000136321 773__ $$g430 (2024), 117210 [16 pp.]$$pComput. methods appl. mech. eng.$$tComputer Methods in Applied Mechanics and Engineering$$x0045-7825
000136321 8564_ $$s3084182$$uhttps://zaguan.unizar.es/record/136321/files/texto_completo.pdf$$yVersión publicada
000136321 8564_ $$s1988447$$uhttps://zaguan.unizar.es/record/136321/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000136321 909CO $$ooai:zaguan.unizar.es:136321$$particulos$$pdriver
000136321 951__ $$a2024-07-31-09:22:55
000136321 980__ $$aARTICLE