{"id":83171,"date":"2025-02-12T03:18:10","date_gmt":"2025-02-12T03:18:10","guid":{"rendered":"https:\/\/peraltafinancing.com\/animation\/accelerate-neural-subspace-based-reduced-order-solver-of-deformable-simulation-by-lipschitz-optimization-physics-based-animation\/"},"modified":"2025-02-12T03:18:10","modified_gmt":"2025-02-12T03:18:10","slug":"accelerate-neural-subspace-based-reduced-order-solver-of-deformable-simulation-by-lipschitz-optimization-physics-based-animation","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=83171","title":{"rendered":"Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization \u00bb Physics-Based Animation"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"post-4091\">\n<div class=\"itemtext\">\n<p>Aoran Lyu, Shixian Zhao, Chuhua Xian, Zhihao Cen, Hongmin Cai, Guoxin Fang<\/p>\n<p>Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs, and recently developed neural-network-based methods with nonlinear subspaces have been proven effective in diverse applications as more concise subspaces can be detected. However, the complexity and landscape of simulation objectives within the subspace have not been optimized, which leaves room for enhancement of the convergence speed. This work focuses on this point by proposing a general method for finding optimized subspace mappings, enabling further acceleration of neural reduced-order simulations while capturing comprehensive representations of the configuration manifolds. We achieve this by optimizing the Lipschitz energy of the elasticity term in the simulation objective, and incorporating the cubature approximation into the training process to manage the high memory and time demands associated with optimizing the newly introduced energy. Our method is versatile and applicable to both supervised and unsupervised settings for optimizing the parameterizations of the configuration manifolds. We demonstrate the effectiveness of our approach through general cases in both quasi-static and dynamics simulations. Our method achieves acceleration factors of up to 6.83 while consistently preserving comparable simulation accuracy in various cases, including large twisting, bending, and rotational deformations with collision handling. This novel approach offers significant potential for accelerating physical simulations, and can be a good add-on to existing neural-network-based solutions in modeling complex deformable objects.<\/p>\n<p><a href=\"https:\/\/lip-neural-subspace.github.io\/\">Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization<\/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\/2024\/11\/21\/accelerate-neural-subspace-based-reduced-order-solver-of-deformable-simulation-by-lipschitz-optimization\/\">November 21st, 2024<\/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\/2024\/11\/21\/accelerate-neural-subspace-based-reduced-order-solver-of-deformable-simulation-by-lipschitz-optimization\/\"\n    dc:identifier=\"https:\/\/www.physicsbasedanimation.com\/2024\/11\/21\/accelerate-neural-subspace-based-reduced-order-solver-of-deformable-simulation-by-lipschitz-optimization\/\"\n    dc:title=\"Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization\"\n    trackback:ping=\"https:\/\/www.physicsbasedanimation.com\/2024\/11\/21\/accelerate-neural-subspace-based-reduced-order-solver-of-deformable-simulation-by-lipschitz-optimization\/trackback\/\" \/>\n<\/rdf:RDF>\t\t\t-->\n\t\t<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Aoran Lyu, Shixian Zhao, Chuhua Xian, Zhihao Cen, Hongmin Cai, Guoxin Fang Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs, and recently developed neural-network-based methods with nonlinear subspaces have been proven effective in diverse applications as more concise subspaces can be detected. However, the complexity and landscape of simulation objectives [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":4302,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12035],"tags":[31589,6119,41365,41366,23565,23065,18075,41363,6283,41364,41362],"dealstore":[],"offerexpiration":[],"class_list":["post-83171","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-animation","tag-accelerate","tag-animation","tag-deformable","tag-lipschitz","tag-neural","tag-optimization","tag-physicsbased","tag-reducedorder","tag-simulation","tag-solver","tag-subspacebased"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization \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=83171\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization \u00bb Physics-Based Animation - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Aoran Lyu, Shixian Zhao, Chuhua Xian, Zhihao Cen, Hongmin Cai, Guoxin Fang Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs, and recently developed neural-network-based methods with nonlinear subspaces have been proven effective in diverse applications as more concise subspaces can be detected. However, the complexity and landscape of simulation objectives [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fivemor.com\/?p=83171\" \/>\n<meta property=\"og:site_name\" content=\"Som2ny Network\" \/>\n<meta property=\"article:published_time\" content=\"2025-02-12T03:18:10+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fivemor.com\/wp-content\/uploads\/2025\/01\/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=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fivemor.com\/?p=83171#article\",\"isPartOf\":{\"@id\":\"https:\/\/fivemor.com\/?p=83171\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/fivemor.com\/#\/schema\/person\/b85e3c3dc0e1daea076524dc8810c371\"},\"headline\":\"Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization \u00bb Physics-Based Animation\",\"datePublished\":\"2025-02-12T03:18:10+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fivemor.com\/?p=83171\"},\"wordCount\":265,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fivemor.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/fivemor.com\/?p=83171#primaryimage\"},\"thumbnailUrl\":\"https:\/\/fivemor.com\/wp-content\/uploads\/2025\/01\/blank.jpg\",\"keywords\":[\"Accelerate\",\"Animation\",\"Deformable\",\"Lipschitz\",\"Neural\",\"Optimization\",\"PhysicsBased\",\"ReducedOrder\",\"Simulation\",\"Solver\",\"SubspaceBased\"],\"articleSection\":[\"Animation\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fivemor.com\/?p=83171#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fivemor.com\/?p=83171\",\"url\":\"https:\/\/fivemor.com\/?p=83171\",\"name\":\"Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization \u00bb Physics-Based Animation - 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