{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:06:51Z","timestamp":1780301211813,"version":"3.54.0"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":31,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306032"],"award-info":[{"award-number":["62306032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515110350"],"award-info":[{"award-number":["2022A1515110350"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008778","name":"University of Science and Technology Beijing","doi-asserted-by":"publisher","award":["FRF\u2010IDRY\u2010GD24\u2010003"],"award-info":[{"award-number":["FRF\u2010IDRY\u2010GD24\u2010003"]}],"id":[{"id":"10.13039\/501100008778","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Animation &amp;amp; Virtual"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Traditional physics\u2010based fluid simulations typically rely on manual modeling and incremental adjustments to achieve desired effects, which can limit objectivity and generalizability to new scenarios. To address these challenges, we propose a novel neural fluid simulator that integrates visual priors from 2D image sequences with physically constrained continuous convolution. Specifically, we extract and refine point clouds from image sequences, then infer the kinetic properties of the fluid. We introduce an energy\u2010based physical constraint and incorporate it into a continuous convolution solver. By iteratively optimizing these inputs to enforce physical laws\u2014particularly incompressibility\u2014the solver produces accurate fluid motion predictions. Our approach uniquely combines visual data and physical constraints, enhancing the realism and accuracy while providing stronger generalization of fluid simulations.<\/jats:p>","DOI":"10.1002\/cav.70115","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:46:35Z","timestamp":1780299995000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural Fluid Simulator With Hybrid Physical\u2010Visual Constraints"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0056-5426","authenticated-orcid":false,"given":"Feilong","family":"Du","sequence":"first","affiliation":[{"name":"School of Intelligent Science and Technology University of Science and Technology Beijing  Beijing China"},{"name":"School of Data Science and Artificial Intelligence Beijing University of Chinese Medicine  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9142-3276","authenticated-orcid":false,"given":"Xiaojuan","family":"Ban","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Technology University of Science and Technology Beijing  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Technology University of Science and Technology Beijing  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelos","family":"Chatzimparmpas","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences Utrecht University  Utrecht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yalan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Technology University of Science and Technology Beijing  Beijing China"},{"name":"Shunde Innovation School University of Science and Technology Beijing  Beijing Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"121","volume-title":"Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques","author":"Stam J.","year":"1999"},{"issue":"1","key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"10623","DOI":"10.1038\/s41598-021-90201-x","article-title":"Prediction of Velocity Profile of Water Based Copper Nanofluid in a Heated Porous Tube Using CFD and Genetic Algorithm","volume":"11","author":"Ciano T.","year":"2021","journal-title":"Scientific Reports"},{"key":"e_1_2_12_4_1","first-page":"5927","article-title":"Visual Grounding of Learned Physical Models","author":"Li Y.","year":"2020","journal-title":"PMLR"},{"key":"e_1_2_12_5_1","first-page":"6901","article-title":"Guaranteed Conservation of Momentum for Learning Particle\u2010Based Fluid Dynamics","volume":"35","author":"Prantl L.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025\u20105718\u20101968\u20100242392\u20102"},{"key":"e_1_2_12_7_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-99693-6","volume-title":"Computational Methods for Fluid Dynamics","author":"Ferziger J. 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