{"id":272990,"date":"2025-06-04T15:43:00","date_gmt":"2025-06-04T15:43:00","guid":{"rendered":"https:\/\/peraltafinancing.com\/animation\/adaptive-hybrid-particle-grid-flow-maps-on-gpu-physics-based-animation\/"},"modified":"2025-06-04T15:43:00","modified_gmt":"2025-06-04T15:43:00","slug":"adaptive-hybrid-particle-grid-flow-maps-on-gpu-physics-based-animation","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=272990","title":{"rendered":"Adaptive Hybrid Particle-Grid Flow Maps on GPU \u00bb Physics-Based Animation"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"post-4219\">\n<div class=\"itemtext\">\n<p>Mengdi Wang, Fan Feng, Junlin Li, Bo Zhu<\/p>\n<p>We propose the adaptive hybrid particle-grid flow map method, a novel flow-map approach that leverages Lagrangian particles to simultaneously transport impulse and guide grid adaptation, introducing a fully adaptive flow map-based fluid simulation framework. The core idea of our method is to maintain flow-map trajectories separately on grid nodes and particles: the grid-based representation tracks long-range flow maps at a coarse spatial resolution, while the particle-based representation tracks both long and short-range flow maps, enhanced by their gradients, at a fine resolution. This hybrid Eulerian-Lagrangian flow-map representation naturally enables adaptivity for both advection and projection steps. We implement this method in Cirrus, a GPU-based fluid simulation framework designed for octree-like adaptive grids enhanced with particle trackers. The efficacy of our system is demonstrated through numerical tests and various simulation examples, achieving up to 512x512x2048 effective resolution on an RTX 4090 GPU. We achieve a 1.5 to 2x speedup with our GPU optimization over the Particle Flow Map method on the same hardware, while the adaptive grid implementation offers efficiency gains of one to two orders of magnitude by reducing computational resource requirements. The source code has been made publicly available at: <a href=\"https:\/\/wang-mengdi.github.io\/proj\/25-cirrus\/\">https:\/\/wang-mengdi.github.io\/proj\/25-cirrus\/<\/a>.<\/p>\n<p><a href=\"https:\/\/wang-mengdi.github.io\/proj\/25-cirrus\/\"><em>Cirrus<\/em>: Adaptive Hybrid Particle-Grid Flow Maps on GPU<\/a><\/p>\n<\/p><\/div>\n<div class=\"postinfo\">\n\t\t\t<span class=\"postmeta\">Posted by christopherbatty on <a href=\"https:\/\/www.physicsbasedanimation.com\/2025\/06\/03\/cirrus-adaptive-hybrid-particle-grid-flow-maps-on-gpu\/\">June 3rd, 2025<\/a> <\/span><br \/>\n\t\t\t\t\t\t<span class=\"postcats\">Posted in <a href=\"https:\/\/www.physicsbasedanimation.com\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a><\/span><br \/>\n\t\t\t\t\t\t\t\t\t<span class=\"posttags\"\/>\n\t\t\t\t<\/div>\n<p>\t\t\t\t\t<!--\n\t\t\t\t<rdf:RDF xmlns:rdf=\"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#\"\n\t\t\txmlns:dc=\"http:\/\/purl.org\/dc\/elements\/1.1\/\"\n\t\t\txmlns:trackback=\"http:\/\/madskills.com\/public\/xml\/rss\/module\/trackback\/\">\n\t\t<rdf:Description rdf:about=\"https:\/\/www.physicsbasedanimation.com\/2025\/06\/03\/cirrus-adaptive-hybrid-particle-grid-flow-maps-on-gpu\/\"\n    dc:identifier=\"https:\/\/www.physicsbasedanimation.com\/2025\/06\/03\/cirrus-adaptive-hybrid-particle-grid-flow-maps-on-gpu\/\"\n    dc:title=\"Cirrus: Adaptive Hybrid Particle-Grid Flow Maps on GPU\"\n    trackback:ping=\"https:\/\/www.physicsbasedanimation.com\/2025\/06\/03\/cirrus-adaptive-hybrid-particle-grid-flow-maps-on-gpu\/trackback\/\" \/>\n<\/rdf:RDF>\t\t\t-->\n\t\t<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Mengdi Wang, Fan Feng, Junlin Li, Bo Zhu We propose the adaptive hybrid particle-grid flow map method, a novel flow-map approach that leverages Lagrangian particles to simultaneously transport impulse and guide grid adaptation, introducing a fully adaptive flow map-based fluid simulation framework. The core idea of our method is to maintain flow-map trajectories separately on [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":245146,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12035],"tags":[17108,6119,3313,38105,1186,17349,99255,18075],"dealstore":[],"offerexpiration":[],"class_list":["post-272990","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-animation","tag-adaptive","tag-animation","tag-flow","tag-gpu","tag-hybrid","tag-maps","tag-particlegrid","tag-physicsbased"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Adaptive Hybrid Particle-Grid Flow Maps on GPU \u00bb Physics-Based Animation - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=272990\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Adaptive Hybrid Particle-Grid Flow Maps on GPU \u00bb Physics-Based Animation - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Mengdi Wang, Fan Feng, Junlin Li, Bo Zhu We propose the adaptive hybrid particle-grid flow map method, a novel flow-map approach that leverages Lagrangian particles to simultaneously transport impulse and guide grid adaptation, introducing a fully adaptive flow map-based fluid simulation framework. 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