{"id":315612,"date":"2025-11-24T12:54:51","date_gmt":"2025-11-24T12:54:51","guid":{"rendered":"https:\/\/peraltafinancing.com\/animation\/precise-discontinuities-in-neural-fields-for-physics-simulation-physics-based-animation\/"},"modified":"2025-11-24T12:54:51","modified_gmt":"2025-11-24T12:54:51","slug":"precise-discontinuities-in-neural-fields-for-physics-simulation-physics-based-animation","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=315612","title":{"rendered":"Precise Discontinuities in Neural Fields for Physics Simulation \u2013 Physics-Based Animation"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"post-4307\">\n\t<!-- .entry-header --><\/p>\n<div class=\"entry-content\">\n<p>Yue Chang, Mengfei Liu, Zhecheng Wang, Peter Yichen Chen, Eitan Grinspun<\/p>\n<p>Cutting thin-walled deformable structures is common in daily life, but poses significant challenges for simulation due to the introduced spatial discontinuities. Traditional methods rely on mesh-based domain representations, which require frequent remeshing and refinement to accurately capture evolving discontinuities. These challenges are further compounded in reduced-space simulations, where the basis functions are inherently geometry- and mesh-dependent, making it difficult or even impossible for the basis to represent the diverse family of discontinuities introduced by cuts. Recent advances in representing basis functions with neural fields offer a promising alternative, leveraging their discretization-agnostic nature to represent deformations across varying geometries. However, the inherent continuity of neural fields is an obstruction to generalization, particularly if discontinuities are encoded in neural network weights. We present Wind Lifter, a novel neural representation designed to ac-<br \/>curately model complex cuts in thin-walled deformable structures. Our approach constructs neural fields that reproduce discontinuities precisely at specified locations, without \u201cbaking in\u201d the position of the cut line. To achieve this, we augment the input coordinates of the neural field with the generalized winding number of any given cut line, effectively lifting the input from two to three dimensions. Lifting allows the network to focus on the easier problem of learning a 3D everywhere-continuous volumetric field, while a corresponding restriction operator enables the final output field to precisely resolve strict discontinuities. Crucially, our approach does not embed the discontinuity in the neural network\u2019s weights, opening avenues to generalization of cut placement. Our method achieves real-time simulation speeds and supports dynamic updates to cut line geometry during the simulation. Moreover, the explicit representation of discontinuities makes our neural field intuitive to control and edit, offering a significant advantage over traditional neural fields, where discontinuities are embedded within the network\u2019s weights, and enabling new applications that rely on general cut placement.<\/p>\n<p><a href=\"https:\/\/www.dgp.toronto.edu\/projects\/windlifter\/\">Lifting the Winding Number: Precise Discontinuities in Neural Fields for Physics Simulation<\/a><\/p>\n<\/p><\/div>\n<p><!-- .entry-content --><\/p>\n<p>\t<!-- .entry-footer -->\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Yue Chang, Mengfei Liu, Zhecheng Wang, Peter Yichen Chen, Eitan Grinspun Cutting thin-walled deformable structures is common in daily life, but poses significant challenges for simulation due to the introduced spatial discontinuities. Traditional methods rely on mesh-based domain representations, which require frequent remeshing and refinement to accurately capture evolving discontinuities. These challenges are further compounded [&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":[6119,145073,16121,23565,23047,18075,3771,6283],"dealstore":[],"offerexpiration":[],"class_list":["post-315612","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-animation","tag-animation","tag-discontinuities","tag-fields","tag-neural","tag-physics","tag-physicsbased","tag-precise","tag-simulation"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Precise Discontinuities in Neural Fields for Physics Simulation \u2013 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=315612\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Precise Discontinuities in Neural Fields for Physics Simulation \u2013 Physics-Based Animation - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Yue Chang, Mengfei Liu, Zhecheng Wang, Peter Yichen Chen, Eitan Grinspun Cutting thin-walled deformable structures is common in daily life, but poses significant challenges for simulation due to the introduced spatial discontinuities. Traditional methods rely on mesh-based domain representations, which require frequent remeshing and refinement to accurately capture evolving discontinuities. These challenges are further compounded [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fivemor.com\/?p=315612\" \/>\n<meta property=\"og:site_name\" content=\"Som2ny Network\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-24T12:54:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fivemor.com\/wp-content\/uploads\/2025\/05\/blank.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"200\" \/>\n\t<meta property=\"og:image:height\" content=\"200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fivemor.com\/?p=315612#article\",\"isPartOf\":{\"@id\":\"https:\/\/fivemor.com\/?p=315612\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/fivemor.com\/#\/schema\/person\/b85e3c3dc0e1daea076524dc8810c371\"},\"headline\":\"Precise Discontinuities in Neural Fields for Physics Simulation \u2013 Physics-Based Animation\",\"datePublished\":\"2025-11-24T12:54:51+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fivemor.com\/?p=315612\"},\"wordCount\":336,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fivemor.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/fivemor.com\/?p=315612#primaryimage\"},\"thumbnailUrl\":\"https:\/\/fivemor.com\/wp-content\/uploads\/2025\/05\/blank.jpg\",\"keywords\":[\"Animation\",\"Discontinuities\",\"Fields\",\"Neural\",\"Physics\",\"PhysicsBased\",\"Precise\",\"Simulation\"],\"articleSection\":[\"Animation\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fivemor.com\/?p=315612#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fivemor.com\/?p=315612\",\"url\":\"https:\/\/fivemor.com\/?p=315612\",\"name\":\"Precise Discontinuities in Neural Fields for Physics Simulation \u2013 Physics-Based Animation - 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