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Graph."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Incremental Potential Contact (IPC) is a widely used, robust, and accurate method for simulating complex frictional contact behaviors. However, achieving high efficiency remains a major challenge, particularly as material stiffness increases, which leads to slower Preconditioned Conjugate Gradient (PCG) convergence, even with state-of-the-art preconditioners. In this article, we propose a fully GPU-optimized IPC simulation framework capable of handling materials across a wide range of stiffnesses, delivering consistent high performance and scalability with up to 10\u00d7 speedup over state-of-the-art GPU IPC methods. Our framework introduces three key innovations: (1) A novel connectivity-enhanced Multilevel Additive Schwarz (MAS) preconditioner on the GPU, designed to efficiently capture both stiff and soft elastodynamics and improve PCG convergence at a reduced preconditioning cost. (2) A\n            <jats:italic>C<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            -continuous cubic energy with an analytic eigensystem for inexact strain limiting, enabling more parallel-friendly simulations of stiff membranes, such as cloth, without membrane locking. (3) For extremely stiff behaviors where elastic waves are barely visible, we employ affine body dynamics (ABD) with a hash-based two-level reduction strategy for fast Hessian assembly and efficient affine-deformable coupling. We conduct extensive performance analyses and benchmark studies to compare our framework against state-of-the-art methods and alternative design choices. Our system consistently delivers the fastest performance across soft, stiff, and hybrid simulation scenarios, even in cases with high resolution, large deformations, and high-speed impacts.\n          <\/jats:p>","DOI":"10.1145\/3735126","type":"journal-article","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T11:14:43Z","timestamp":1746616483000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["StiffGIPC: Advancing GPU IPC for Stiff Affine-Deformable Simulation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9147-2289","authenticated-orcid":false,"given":"Kemeng","family":"Huang","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States, The University of Hong Kong, Hong Kong, Hong Kong, and TransGP, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8744-9294","authenticated-orcid":false,"given":"Xinyu","family":"Lu","sequence":"additional","affiliation":[{"name":"TransGP, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4446-1442","authenticated-orcid":false,"given":"Huancheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States, The University of Hong Kong, Hong Kong, Hong Kong, and TransGP, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2729-5860","authenticated-orcid":false,"given":"Taku","family":"Komura","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong, Hong Kong and TransGP, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9868-7311","authenticated-orcid":false,"given":"Minchen","family":"Li","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1778819"},{"key":"e_1_3_4_3_1","doi-asserted-by":"crossref","unstructured":"Ryoichi Ando. 2024. 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