Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Li, Minchen"'
Publikováno v:
ACM Transactions on Graphics, Vol. 43, No. 6, Article 225, 2024
We propose a GPU-based iterative method for accelerated elastodynamic simulation with the log-barrier-based contact model. While Newton's method is a conventional choice for solving the interior-point system, the presence of ill-conditioned log barri
Externí odkaz:
http://arxiv.org/abs/2407.00046
Autor:
Jiang, Ying, Yu, Chang, Xie, Tianyi, Li, Xuan, Feng, Yutao, Wang, Huamin, Li, Minchen, Lau, Henry, Gao, Feng, Yang, Yin, Jiang, Chenfanfu
As consumer Virtual Reality (VR) and Mixed Reality (MR) technologies gain momentum, there's a growing focus on the development of engagements with 3D virtual content. Unfortunately, traditional techniques for content creation, editing, and interactio
Externí odkaz:
http://arxiv.org/abs/2401.16663
Autor:
Cao, Yadi, Zhao, Yidong, Li, Minchen, Yang, Yin, Choo, Jinhyun, Terzopoulos, Demetri, Jiang, Chenfanfu
Publikováno v:
Comput. Mech. 2024. Unstructured moving least squares material point methods: a stable kernel approach with continuous gradient reconstruction on general unstructured tessellations
The Material Point Method (MPM) is a hybrid Eulerian Lagrangian simulation technique for solid mechanics with significant deformation. Structured background grids are commonly employed in the standard MPM, but they may give rise to several accuracy p
Externí odkaz:
http://arxiv.org/abs/2312.10338
Autor:
Zong, Zeshun, Li, Xuan, Li, Minchen, Chiaramonte, Maurizio M., Matusik, Wojciech, Grinspun, Eitan, Carlberg, Kevin, Jiang, Chenfanfu, Chen, Peter Yichen
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasti
Externí odkaz:
http://arxiv.org/abs/2310.17790
Autor:
Li, Minchen, Ferguson, Zachary, Schneider, Teseo, Langlois, Timothy, Zorin, Denis, Panozzo, Daniele, Jiang, Chenfanfu, Kaufman, Danny M.
Recent advances in the simulation of frictionally contacting elastodynamics with the Incremental Potential Contact (IPC) model have enabled inversion and intersection-free simulation via the application of mollified barriers, filtered line-search, an
Externí odkaz:
http://arxiv.org/abs/2307.15908
Autor:
Liu, Hangxin, Zhang, Zeyu, Jiao, Ziyuan, Zhang, Zhenliang, Li, Minchen, Jiang, Chenfanfu, Zhu, Yixin, Zhu, Song-Chun
In this work, we present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks. To achieve various down
Externí odkaz:
http://arxiv.org/abs/2301.05821
We present a robust and efficient method for simulating Lagrangian solid-fluid coupling based on a new operator splitting strategy. We use variational formulations to approximate fluid properties and solid-fluid interactions, and introduce a unified
Externí odkaz:
http://arxiv.org/abs/2301.01976
Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks. High-quality Text-to-video (T2V), a task that has been long considered mission-impossible, was proven feasible with reasonably
Externí odkaz:
http://arxiv.org/abs/2211.13887
Learning the physical simulation on large-scale meshes with flat Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity w.r.t. the number of nodes and over-smoothing. There has been growing inter
Externí odkaz:
http://arxiv.org/abs/2210.02573
We introduce Midas, a robotics simulation framework based on the Incremental Potential Contact (IPC) model. Our simulator guarantees intersection-free, stable, and accurate resolution of frictional contact. We demonstrate the efficacy of our framewor
Externí odkaz:
http://arxiv.org/abs/2210.00130