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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20250110T023313Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241206T090000
DTEND;TZID=Asia/Tokyo:20241206T101000
UID:siggraphasia_SIGGRAPH Asia 2024_sess141@linklings.com
SUMMARY:Elastics / Solvers / Neural Physics
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nMiNNIE: a Mixed Multigrid Method for Real-time Simulation of Nonlinear
  Near-Incompressible Elastics\n\nWe propose MiNNIE, a simple yet comprehen
 sive framework for real-time simulation of nonlinear near-incompressible e
 lastics. To avoid the common volumetric locking issues at high Poisson's r
 atios of linear finite element methods (FEM), we build MiNNIE upon a mixed
  FEM framework and further incorporat...\n\n\nLiangwang Ruan (Peking Unive
 rsity), Bin Wang (Beijing Institute for General Artificial Intelligence), 
 Tiantian Liu (Taichi Graphics), and Baoquan Chen (Peking University)\n----
 -----------------\nAnalytic rotation-invariant modelling of anisotropic fi
 nite elements\n\nA new formulation anisotropic elasticity is presented, wh
 ich also generalizes to isotropy in low-order invariant-expressible form a
 nd with robust behaviour near singularity-inducing states. As a result, we
  can rewrite, simplify and speedup several existing anisotropic and isotro
 pic distortion energi...\n\n\nHuancheng Lin, Floyd M. Chitalu, and Taku Ko
 mura (University of Hong Kong)\n---------------------\nAccelerate Neural S
 ubspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz O
 ptimization\n\nReduced-order simulation is an emerging method for accelera
 ting physical simulations with high DOFs, and recently developed neural-ne
 twork-based methods with nonlinear subspaces have been proven effective in
  diverse applications as more concise subspaces can be detected. However, 
 the complexity and ...\n\n\nAoran Lyu (South China University of Technolog
 y, University of Manchester); Shixian Zhao, Chuhua Xian, Zhihao Cen, and H
 ongmin Cai (South China University of Technology); and Guoxin Fang (Chines
 e University of Hong Kong)\n---------------------\nNeural Implicit Reduced
  Fluid Simulation\n\nHigh-fidelity simulation of fluid dynamics is challen
 ging because of the high dimensional state data needed to capture fine det
 ails and the large computational cost associated with advancing the system
  in time. We present neural implicit reduced fluid simulation (NIRFS), a r
 educed fluid simulation t...\n\n\nYuanyuan Tao (McGill University, Huawei 
 Canada); Ivan Puhachov (Université de Montréal); and Derek Nowrouzezahrai 
 and Paul Kry (McGill University)\n---------------------\nTrust-Region Eige
 nvalue Filtering for Projected Newton\n\nWe introduce a novel adaptive eig
 envalue filtering strategy to stabilize and accelerate the optimization of
  Neo-Hookean energy and its variants under the Projected Newton framework.
  For the first time, we show that Newton’s method, Projected Newton with e
 igenvalue clamping and Projected Newton...\n\n\nHonglin Chen (Columbia Uni
 versity); Hsueh-Ti Derek Liu (Roblox, University of British Columbia); Ale
 c Jacobson (University of Toronto, Adobe Research); David I.W. Levin (Univ
 ersity of Toronto, NVIDIA); and Changxi Zheng (Columbia University)\n-----
 ----------------\nNeural Garment Dynamic Super-Resolution\n\nAchieving eff
 icient, high-fidelity, high-resolution garment simulation is challenging d
 ue to its computational demands. Conversely, low-resolution garment simula
 tion is more accessible and ideal for low-budget devices like smartphones.
  In this paper, we introduce a lightweight, learning-based method...\n\n\n
 Meng Zhang and Jun Li (Nanjing University of Science and Technology)\n\nRe
 gistration Category: Full Access, Full Access Supporter\n\nLanguage Format
 : English Language\n\nSession Chair: Sheldon Andrews (École de technologie
  supérieure (ÉTS))
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