GAMES Webinar 2019 – 109期 | 李旻辰(宾夕法尼亚大学)，胡译心（纽约大学）
【GAMES Webinar 2019-109期】
主持人：吕琳，山东大学（个人主页：http://irc.cs.sdu.edu.cn/~lulin/）
报告嘉宾1：李旻辰，宾夕法尼亚大学
报告时间：2019年9月5日 晚8:00-8:45（北京时间）
报告题目：Decomposed Optimization Time Integrator for Large-Step Elastodynamics
报告摘要：
Simulation methods are rapidly advancing the accuracy, consistency and controllability of elastodynamic modeling and animation. Critical to these advances, we require efficient time step solvers that reliably solve all implicit time integration problems for elastica. While available time step solvers succeed admirably in some regimes, they become impractically slow, inaccurate, unstable, or even divergent in others — as we show here. Towards addressing these needs we present the Decomposed Optimization Time Integrator (DOT), a new domain-decomposed optimization method for solving the per time step, nonlinear problems of implicit numerical time integration. DOT is especially suitable for large time step simulations of deformable bodies with nonlinear materials and high-speed dynamics. It is efficient, automated, and robust at large, fixed-size time steps, thus ensuring stable, continued progress of high-quality simulation output. Across a broad range of extreme and mild deformation dynamics, using frame-rate size time steps with widely varying object shapes and mesh resolutions, we show that DOT always converges to user-set tolerances, generally well-exceeding and always close to the best wall-clock times across all previous nonlinear time step solvers, irrespective of the deformation applied.
讲者简介：
李旻辰，宾夕法尼亚大学计算机与信息科学博士在读，师从蒋陈凡夫教授，主要研究优化问题和数值模拟在几何处理与计算机动画中的应用。他2015年本科毕业于浙江大学竺可桢学院混合班，主修计算机科学与技术，并于2018年获得英属哥伦比亚大学计算机科学硕士，曾多次在Adobe研究院参与科研实习。
讲者个人主页：http://www.seas.upenn.edu/~minchenl/
报告嘉宾2：胡译心，纽约大学
报告时间：2019年9月5日 晚8:45-9:30（北京时间）
报告题目：TriWild: Robust Triangulation with Curve Constraints
报告摘要：
We propose a robust 2D meshing algorithm, TriWild, to generate curved triangles reproducing smooth feature curves, leading to coarse meshes designed to match the simulation requirements necessary by applications and avoiding the geometrical errors introduced by linear meshes. The robustness and effectiveness of our technique are demonstrated by batch processing an SVG collection of 20k images, and by comparing our results against state of the art linear and curvilinear meshing algorithms. We demonstrate for our algorithm the practical utility of computing diffusion curves, fluid simulations, elastic deformations, and shape inflation on complex 2D geometries.
讲者简介：
Yixin Hu is a Computer Science Ph.D. student at Courant Institute of Mathematical Sciences of New York University where she joined Geometric Computing Lab and started working with professor Daniele Panozzo after she got my Bachelor’s degree in Computer Science at Zhejiang University, China, in 2016.
Her research interests are Computer Graphics and Geometry Processing. Her current research direction is triangular/tetrahedral meshing and its application, e.g. FEM-based physical simulation. She would love to explore more machine learning or medical related applications of triangle meshes and terahedral meshes.
讲者个人主页：https://cs.nyu.edu/~yixinhu/
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