Thermodynamics-informed super-resolution of scarce temporal dynamics data

Bermejo-Barbanoj, Carlos (Universidad de Zaragoza) ; Moya, Beatriz ; Badías, Alberto ; Chinesta, Francisco ; Cueto, Elías (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.cma.2024.117210

Año: 2024

Publicado en: Computer Methods in Applied Mechanics and Engineering 430 (2024), 117210 [16 pp.]
ISSN: 0045-7825

Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033
Financiación: info:eu-repo/grantAgreement/ES/MTFP/TSI-100930-2023-1

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)