{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:09:20Z","timestamp":1767982160570,"version":"3.49.0"},"reference-count":81,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Spanish Ministry of Economy and Competitiveness","award":["PID2020-113463RB-C31"],"award-info":[{"award-number":["PID2020-113463RB-C31"]}]},{"name":"Regional Government of Aragon and the European Social Fund"},{"name":"Research Group T88"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,2,1]]},"DOI":"10.1109\/tpami.2022.3160100","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T19:50:33Z","timestamp":1647978633000},"page":"2136-2150","source":"Crossref","is-referenced-by-count":13,"title":["Physics Perception in Sloshing Scenes With Guaranteed Thermodynamic Consistency"],"prefix":"10.1109","volume":"45","author":[{"given":"Beatriz","family":"Moya","sequence":"first","affiliation":[{"name":"Aragon Institute of Engineering Research, Universidad de Zaragoza, Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7639-6767","authenticated-orcid":false,"given":"Alberto","family":"Badias","sequence":"additional","affiliation":[{"name":"Polytechnic University of Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3003-5856","authenticated-orcid":false,"given":"David","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, Universidad de Zaragoza, Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6272-3429","authenticated-orcid":false,"given":"Francisco","family":"Chinesta","sequence":"additional","affiliation":[{"name":"ESI Group Chair, PIMM Lab, Arts et Métiers Institute of Technology, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1017-4381","authenticated-orcid":false,"given":"Elias","family":"Cueto","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, Universidad de Zaragoza, Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-072220-093055"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007210"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1306572110"},{"key":"ref4","first-page":"317","article-title":"SPNets: Differentiable fluid dynamics for deep neural networks","volume-title":"Proc. Conf. Robot Learn.","author":"Schenck"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.126.036401"},{"key":"ref6","article-title":"Physics informed deep learning (Part I): Data-driven solutions of nonlinear partial differential equations","author":"Raissi","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109913"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.126.180604"},{"key":"ref9","article-title":"Weak form generalized hamiltonian learning","author":"Course","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41217-7_17"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.36884\/jafm.13.05.30909"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s00162-020-00520-4"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-020-09404-6"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0234569"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.56.6620"},{"key":"ref16","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989247"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3095269"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2969931"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)EM.1943-7889.0001947"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevFluids.4.103907"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.taml.2020.01.031"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2020.0097"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109339"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0077-5"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-07210-0"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.113816"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3148734"},{"key":"ref30","article-title":"Rank-adaptive structure-preserving reduced basis methods for hamiltonian systems","author":"Hesthaven","year":"2020"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-77957-3_16"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1103\/physrevfluids.6.114402"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s00466-017-1440-1"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s00161-018-0677-z"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109982"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3390\/e21121165"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s00466-019-01705-3"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.113763"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109950"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13619"},{"key":"ref41","first-page":"3424","article-title":"Accelerating eulerian fluid simulation with convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tompson"},{"key":"ref42","article-title":"An efficient deep learning technique for the navier-stokes equations: Application to unsteady wake flow dynamics","author":"Miyanawala","year":"2017"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1063\/5.0030137"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13620"},{"key":"ref45","first-page":"8459","article-title":"Learning to simulate complex physics with graph networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sanchez-Gonzalez"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2019.112789"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.110079"},{"key":"ref48","first-page":"5927","article-title":"Visual grounding of learned physical models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref49","first-page":"127","article-title":"Galileo: Perceiving physical object properties by integrating a physics engine with deep learning","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1177\/0278364917734052"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.aa.30.090192.002551"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-31439-6_534"},{"key":"ref53","article-title":"Detection and tracking of liquids with fully convolutional networks","author":"Schenck","year":"2016"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759326"},{"key":"ref55","article-title":"Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach","author":"Eppel","year":"2016"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1063\/1.5128231"},{"key":"ref57","first-page":"15 379","article-title":"Hamiltonian neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Greydanus"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.08.017"},{"key":"ref59","article-title":"Hamiltonian generative networks","author":"Toth","year":"2019"},{"key":"ref60","article-title":"Symplectic ODE-net: Learning hamiltonian dynamics with control","author":"Zhong","year":"2019"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39895-0_3"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1088\/0034-4885\/29\/1\/306"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.83.4542"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.2514\/1.J058462"},{"key":"ref65","article-title":"Physics-informed autoencoders for lyapunov-stable fluid flow prediction","author":"Erichson","year":"2019"},{"issue":"2011","key":"ref66","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lecture Notes"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1017\/s0956792521000139"},{"key":"ref68","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pascanu"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref71","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014"},{"key":"ref72","volume-title":"ABAQUS\/Standard Users Manual, Version 6.9","author":"Smith","year":"2009"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/S0895-7177(00)00240-5"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/34.232073"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"ref78","article-title":"Depth post-processing for intel realsense\u2122 depth camera d400 series","author":"Anders Grunnet-Jepsen"},{"key":"ref79","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","author":"Qi","year":"2017"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00472"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10008914\/09739979.pdf?arnumber=9739979","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:32:59Z","timestamp":1705537979000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9739979\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":81,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3160100","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]}}}