GAMES Webinar 2022 – 241期(大规模场景的高效多重网格架构与湍流细节预测) | 邵涵(KAUST),柏凯(上海科技大学)

【GAMES Webinar 2022-241期】(模拟与动画专题-大规模场景的高效多重网格架构与湍流细节预测)



报告题目:A Fast Unsmoothed Aggregation Algebraic Multigrid Framework for the Large-Scale Simulation of Incompressible Flow


Multigrid methods are quite efficient for solving the pressure Poisson equation in simulations of incompressible flow. However, for viscous liquids, geometric multigrid turned out to be less efficient for solving the variational viscosity equation. In this contribution, we present an Unsmoothed Aggregation Algebraic MultiGrid (UAAMG) method with a multi-color Gauss-Seidel smoother, which consistently solves the variational viscosity equation in a few iterations for various material parameters. Moreover, we augment the OpenVDB data structure with Intel SIMD intrinsic functions to perform sparse matrix-vector multiplications efficiently on all multigrid levels. Our framework is 2.0 to 14.6 times faster compared to the state-of-the-art adaptive octree solver in commercial software for the large-scale simulation of both non-viscous and viscous flow. The code is available at


Han Shao recently received his Ph.D. degree in June 2022 from King Abdullah University of Science and Technology (KAUST) in the Program of Applied Mathematics and Computational Sciences. He worked under the supervision of Professor Dominik L. Michels from 2017 to 2022. His academic work aims for accelerating physically-based simulations using principled mathematical modeling as well as data-driven approaches. Previously, he received a M.Sc. degree in Mechanical Engineering from KAUST and a B.Sc. degree in Theoretical and Applied Mechanics from the University of Science and Technology of China. He also holds a M.Sc. in Computer Science from Georgia Institute of Technology (OMSCS).




报告题目:Predicting High-Resolution Turbulence Details in Space and Time


Predicting the fine and intricate details of a turbulent flow field in both space and time from a coarse input remains a major challenge despite the availability of modern machine learning tools. In this paper, we present a simple and effective dictionary-based approach to spatio-temporal upsampling of fluid simulation. We demonstrate that our neural network approach can reproduce the visual complexity of turbulent flows from spatially and temporally coarse velocity fields even when using a generic training set. Moreover, since our method generates finer spatial and/or temporal details through embarrassingly-parallel upsampling of small local patches, it can efficiently predict high-resolution turbulence details across a variety of grid resolutions. As a consequence, our method offers a whole range of applications varying from fluid flow upsampling to fluid data compression. We demonstrate the efficiency and generalizability of our method for synthesizing turbulent flows on a series of complex examples, highlighting dramatically better results in spatio-temporal upsampling and flow data compression than existing methods as assessed by both qualitative and quantitative comparisons.


Kai Bai received his Ph.D. at ShanghaiTech University under the supervision of Professor Xiaopei Liu. He was interested in numerical simulation of fluid flows, machine learning, computer graphics, as well as their applications to fast fluid flow simulation and fluid data compression. He was also applying the lattice Boltzmann method for simulating blood flows for medical diagnosis.



王笑琨,北京科技大学计算机与通信工程学院副教授,英国国家计算机动画中心Marie-Curie Fellow,入选北京市科协青年人才托举工程。CCF CAD&CG专委会、CAAI智慧医疗专委会、CSIG人机交互专委会等委员。主要研究方向包括计算机图形学,物理仿真,医疗辅助模拟分析等,在IEEE VR、IEEE BIBM、SCA、CGI、CGF、TVC等主流期刊会议上发表论文30余篇。曾获CGI2020最佳论文奖,CASA 2022 AniNex最佳论文奖,中国黄金协会科技进步一等奖,全国优秀城乡规划设计三等奖。主持承担了欧盟玛丽居里学者奖学金、国家自然科学基金、国家重点研发计划子课题、中国博士后基金等多项课题。


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