A thermodynamics-informed active learning approach to perception and reasoning about fluids

Moya, Beatriz (Universidad de Zaragoza) ; Badías, Alberto ; González, David (Universidad de Zaragoza) ; Chinesta, Francisco ; Cueto, Elías (Universidad de Zaragoza)

Resumen: Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception) and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they have not been trained explicitly.

Idioma: Inglés

DOI: 10.1007/s00466-023-02279-x

Año: 2023

Publicado en: COMPUTATIONAL MECHANICS 72, 3 (2023), 577–591
ISSN: 0178-7675

Factor impacto JCR: 3.7 (2023)

Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 13 / 135 = 0.096 (2023) - Q1 - T1
Categ. JCR: MECHANICS rank: 33 / 170 = 0.194 (2023) - Q1 - T1

Factor impacto SCIMAGO: 1.265 - Applied Mathematics (Q1) - Computational Mathematics (Q1) - Ocean Engineering (Q1) - Computational Theory and Mathematics (Q1) - Mechanical Engineering (Q1) - Computational Mechanics (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033
Financiación: info:eu-repo/grantAgreement/ES/UZ/UZ2019-0060

Tipo y forma: Artículo (Versión definitiva)

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