GAMES Webinar 2026 – 407期(高性能物理仿真)
报告嘉宾：刘嘉枫 浙江大学
报告时间：2026年6月26号 早上10:00-10:30（北京时间）
报告题目：
Distributed Affine Body Dynamics with Adaptive Consensus
报告摘要：
基于增量势接触（Incremental Potential Contact, IPC）框架的仿射体动力学（Affine Body Dynamics, ABD）能够对具有近刚性行为的极高刚度固体进行精确仿真，并严格保证无穿透。然而，IPC 中全局耦合的障碍约束使其难以在多 GPU 和多计算节点上实现可扩展并行求解。为此，本文提出一种基于共识优化的分布式 ABD 求解形式，并采用 ADMM 进行迭代求解。各计算节点并行求解局部 ABD 子问题，随后通过全局共识步骤对共享边界物体的一致性进行约束。该方法在分布式执行条件下保持了 IPC 级别的鲁棒性与全局一致性。实验结果表明，本文方法在多节点大规模场景中能够稳定收敛，保持全程无穿透，并具有接近线性的加速比。
讲者简介：
刘嘉枫，浙江大学CAD&CG全国重点实验室博士生，导师为许威威教授，主要研究方向是基于物理模拟的动画和高性能计算。 在 SIGGRAPH 上发表多篇论文。
讲者主页：https://hanke98.github.io/
报告嘉宾：冯相, 加州大学洛杉矶分校
报告时间：2026年6月26号 早上10:30-11:00（北京时间）
报告题目：
MPM Lite: Linear Kernels and Integration without Particles
报告摘要：
We introduce MPM Lite, a hybrid Lagrangian/Eulerian method that eliminates the need for particle-based quadrature at solve time. Standard Material Point Method (MPM) practices suffer from a performance bottleneck where expensive implicit solves are proportional to particle-per-cell (PPC) counts due to the the choices of particle-based quadrature and wide-stencil kernels. By contrast, MPM Lite treats particles primarily as carriers of kinematic state and material history. Conceptualizing the background Cartesian grid as a voxel hexahedral mesh, we resample particle states onto fixed-location quadrature points using efficient, compact linear kernels. This architectural shift allows force assembly and the entire time-integration process to proceed without accessing particles, thus making the solver’s complexity independent of the particle count. We demonstrate through extensive experiments that MPM Lite preserves the robustness and versatility of traditional MPM across diverse materials while delivering significant speedups in implicit settings while simultaneously improving explicit ones.
讲者简介：
冯相，加州大学洛杉矶分校计算机系博士生，导师为Chenfanfu Jiang教授和Demetri Terzopoulos教授。主要研究方向是机器人智能和物理仿真。
讲者主页：https://f1shel.github.io/
主持人简介：
Yin Yang is currently an Associate Professor with the Kahlert School of Computing at the University of Utah. Before joining the U, he was a faculty member at Clemson University and University of New Mexico. He received Ph.D. degree of Computer Science from The University of Texas, Dallas in 2013 (the awardee of David Daniel Fellowship Prize). He was a Research/Teaching Assistant at UT Dallas as well as UT Southwestern Medical Center. His research mainly focuses on real-time physics-based computer graphics, animation and simulation with a strong emphasis on interdisciplinarity. He was a Research Intern in Microsoft Research Asia in 2012. He received NSF CRII (2015) and CAREER (2019) awards
