报告题目(Title): Descent methods for elastic body simulation on the GPU
报告人姓名(Speaker): Yin Yang （Assistant Professor from The University of New Mexico, USA）
时间(Date&Time): 2018.7.24, 10:30
地点(Location): B240, School of IoT
报告摘要(Abstract): In this presentation, we show that many existing elastic body simulation approaches can be interpreted as descent methods, under a nonlinear optimization framework derived from implicit time integration. The key question is how to find an effective descent direction with a low cost. Based on this observation, we propose a novel gradient descent method using Jacobi preconditioning and Chebyshev acceleration. The convergence rate of this method is comparable to that of L-BFGS or nonlinear conjugate gradient. But unlike other methods, it requires no dot product operation, making it suitable for GPU implementation. To further ensure its convergence and performance, we develop a series of step length adjustment, initialization, and invertible model conversion techniques, all of which are compatible with GPU acceleration. Our experiment shows that the resulting simulator is simple, fast, scalable, memory-efficient, and robust against very large time steps and deformations. It can correctly simulate the deformation behaviors of many elastic materials, as long as their energy functions are second-order differentiable and the Hessian matrices can be quickly evaluated. For additional speedups, the method can serve as a complement to other real-time techniques as well, such as multi-grid.
报告人简介(Biography): Dr. Yin Yang is an Assistant Professor at Electrical and Computer Engineering Department, the University of New Mexico. He received my Ph.D. degree in Computer Science with Department of Computer Science at the University of Texas at Dallas. His current research mostly focuses on computer graphics and related topics. More specifically, His research interests include deformable model, physics-based animation/simulation, medical imaging analysis and visualization.
邀请人 (Inviter): 方伟