125340 20241125101123.0 doi 10.1109/TPAMI.2022.3160100 sideral 128830 ART-2023-128830 eng Moya, Beatriz Universidad de Zaragoza (orcid)0000-0001-5483-6012 Physics perception in sloshing scenes with guaranteed thermodynamic consistency 2023 Access copy available to the general public Unrestricted Physics perception very often faces the problem that only limited data or partial measurements on the scene are available. In this work, we propose a strategy to learn the full state of sloshing liquids from measurements of the free surface. Our approach is based on recurrent neural networks (RNN) that project the limited information available to a reduced-order manifold to not only reconstruct the unknown information but also be capable of performing fluid reasoning about future scenarios in real-time. To obtain physically consistent predictions, we train deep neural networks on the reduced-order manifold that, through the employ of inductive biases, ensure the fulfillment of the principles of thermodynamics. RNNs learn from history the required hidden information to correlate the limited information with the latent space where the simulation occurs. Finally, a decoder returns data to the high-dimensional manifold, to provide the user with insightful information in the form of augmented reality. This algorithm is connected to a computer vision system to test the performance of the proposed methodology with real information, resulting in a system capable of understanding and predicting future states of the observed fluid in real-time. info:eu-repo/grantAgreement/ES/DGA/T88 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 All rights reserved http://www.europeana.eu/rights/rr-f/ 20.8 2023 ENGINEERING, ELECTRICAL & ELECTRONIC 3 / 353 = 0.008 2023 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 2 / 197 = 0.01 2023 Q1 T1 6.158 2023 Applied Mathematics 2023 Q1 Artificial Intelligence 2023 Q1 Software 2023 Q1 Computer Vision and Pattern Recognition 2023 Q1 Computational Theory and Mathematics 2023 Q1 28.4 2023 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Badias, Alberto (orcid)0000-0001-7639-6767 Gonzalez, 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. 45, 2 (2023), 2136-2150 IEEE trans. pattern anal. mach. intell. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 0162-8828 16965267 http://zaguan.unizar.es/record/125340/files/texto_completo.pdf Postprint 1681646 http://zaguan.unizar.es/record/125340/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:125340 articulos driver 2024-11-22-11:57:07 ARTICLE