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DTSTART:18871231T000000
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DTSTAMP:20260817T171542Z
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:Each 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 comprehensive framework for r
 eal-time simulation of nonlinear near-incompressible elastics. To avoid th
 e common volumetric locking issues at high Poisson's ratios of linear fini
 te element methods (FEM), we build MiNNIE upon a mixed FEM framework and f
 urther incorporat...\n\n\nLiangwang Ruan (Peking University), Bin Wang (Be
 ijing Institute for General Artificial Intelligence), Tiantian Liu (Taichi
  Graphics), and Baoquan Chen (Peking University)\n---------------------\nA
 nalytic rotation-invariant modelling of anisotropic finite elements\n\nA n
 ew formulation anisotropic elasticity is presented, which also generalizes
  to isotropy in low-order invariant-expressible form and with robust behav
 iour near singularity-inducing states. As a result, we can rewrite, simpli
 fy and speedup several existing anisotropic and isotropic distortion energ
 i...\n\n\nHuancheng Lin, Floyd M. Chitalu, and Taku Komura (University of 
 Hong Kong)\n---------------------\nTrust-Region Eigenvalue Filtering for P
 rojected Newton\n\nWe introduce a novel adaptive eigenvalue filtering stra
 tegy to stabilize and accelerate the optimization of Neo-Hookean energy an
 d its variants under the Projected Newton framework. For the first time, w
 e show that Newton’s method, Projected Newton with eigenvalue clamping and
  Projected Newton...\n\n\nHonglin Chen (Columbia University); Hsueh-Ti Der
 ek Liu (Roblox, University of British Columbia); Alec Jacobson (University
  of Toronto, Adobe Research); David I.W. Levin (University of Toronto, NVI
 DIA); and Changxi Zheng (Columbia University)\n---------------------\nAcce
 lerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation
  by Lipschitz Optimization\n\nReduced-order simulation is an emerging meth
 od for accelerating physical simulations with high DOFs, and recently deve
 loped neural-network-based methods with nonlinear subspaces have been prov
 en effective in diverse applications as more concise subspaces can be dete
 cted. However, the complexity and ...\n\n\nAoran Lyu (South China Universi
 ty of Technology, University of Manchester); Shixian Zhao, Chuhua Xian, Zh
 ihao Cen, and Hongmin Cai (South China University of Technology); and Guox
 in Fang (Chinese University of Hong Kong)\n---------------------\nNeural I
 mplicit Reduced Fluid Simulation\n\nHigh-fidelity simulation of fluid dyna
 mics is challenging because of the high dimensional state data needed to c
 apture fine details and the large computational cost associated with advan
 cing the system in time. We present neural implicit reduced fluid simulati
 on (NIRFS), a reduced fluid simulation t...\n\n\nYuanyuan Tao (McGill Univ
 ersity, Huawei Canada); Ivan Puhachov (Université de Montréal); and Derek 
 Nowrouzezahrai and Paul Kry (McGill University)\n---------------------\nNe
 ural Garment Dynamic Super-Resolution\n\nAchieving efficient, high-fidelit
 y, high-resolution garment simulation is challenging due to its computatio
 nal demands. Conversely, low-resolution garment simulation is more accessi
 ble and ideal for low-budget devices like smartphones. In this paper, we i
 ntroduce a lightweight, learning-based method...\n\n\nMeng Zhang and Jun L
 i (Nanjing University of Science and Technology)\n\nRegistration Category:
  Full Access, Full Access Supporter\n\nLanguage Format: English Language\n
 \nSession Chair: Sheldon Andrews (École de Technologie Supérieure (ÉTS), M
 cGill University)
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