000124412 001__ 124412
000124412 005__ 20240731103325.0
000124412 0247_ $$2doi$$a10.1007/s00466-023-02279-x
000124412 0248_ $$2sideral$$a132837
000124412 037__ $$aART-2023-132837
000124412 041__ $$aeng
000124412 100__ $$0(orcid)0000-0001-5483-6012$$aMoya, Beatriz$$uUniversidad de Zaragoza
000124412 245__ $$aA thermodynamics-informed active learning approach to perception and reasoning about fluids
000124412 260__ $$c2023
000124412 5060_ $$aAccess copy available to the general public$$fUnrestricted
000124412 5203_ $$aLearning 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.
000124412 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R$$9info:eu-repo/grantAgreement/ES/UZ/UZ2019-0060
000124412 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000124412 590__ $$a3.7$$b2023
000124412 592__ $$a1.265$$b2023
000124412 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b13 / 135 = 0.096$$c2023$$dQ1$$eT1
000124412 593__ $$aApplied Mathematics$$c2023$$dQ1
000124412 591__ $$aMECHANICS$$b33 / 170 = 0.194$$c2023$$dQ1$$eT1
000124412 593__ $$aComputational Mathematics$$c2023$$dQ1
000124412 593__ $$aOcean Engineering$$c2023$$dQ1
000124412 593__ $$aComputational Theory and Mathematics$$c2023$$dQ1
000124412 593__ $$aMechanical Engineering$$c2023$$dQ1
000124412 593__ $$aComputational Mechanics$$c2023$$dQ1
000124412 594__ $$a7.8$$b2023
000124412 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000124412 700__ $$0(orcid)0000-0001-7639-6767$$aBadías, Alberto
000124412 700__ $$0(orcid)0000-0003-3003-5856$$aGonzález, David$$uUniversidad de Zaragoza
000124412 700__ $$aChinesta, Francisco
000124412 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000124412 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000124412 773__ $$g72, 3 (2023), 577–591$$pComput. mech.$$tCOMPUTATIONAL MECHANICS$$x0178-7675
000124412 8564_ $$s4064548$$uhttps://zaguan.unizar.es/record/124412/files/texto_completo.pdf$$yVersión publicada
000124412 8564_ $$s2202526$$uhttps://zaguan.unizar.es/record/124412/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000124412 909CO $$ooai:zaguan.unizar.es:124412$$particulos$$pdriver
000124412 951__ $$a2024-07-31-09:44:25
000124412 980__ $$aARTICLE