124412 20241125101133.0 doi 10.1007/s00466-023-02279-x sideral 132837 ART-2023-132837 eng Moya, Beatriz Universidad de Zaragoza (orcid)0000-0001-5483-6012 A thermodynamics-informed active learning approach to perception and reasoning about fluids 2023 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033 info:eu-repo/grantAgreement/ES/UZ/UZ2019-0060 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.7 2023 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 13 / 135 = 0.096 2023 Q1 T1 MECHANICS 33 / 170 = 0.194 2023 Q1 T1 1.265 2023 Applied Mathematics 2023 Q1 Computational Mathematics 2023 Q1 Ocean Engineering 2023 Q1 Computational Theory and Mathematics 2023 Q1 Mechanical Engineering 2023 Q1 Computational Mechanics 2023 Q1 7.8 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Badías, Alberto (orcid)0000-0001-7639-6767 González, David Universidad de Zaragoza (orcid)0000-0003-3003-5856 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. 72, 3 (2023), 577–591 Comput. mech. COMPUTATIONAL MECHANICS 0178-7675 4064548 http://zaguan.unizar.es/record/124412/files/texto_completo.pdf Versión publicada 2202526 http://zaguan.unizar.es/record/124412/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:124412 articulos driver 2024-11-22-11:59:37 ARTICLE