报告题目：Geometric Machine Learning for Computational Fluid Dynamics

报告人：Nobuyuki Umetani, Autodesk研究院

时间：2018年5月7日下午2:30-4:00

地点：科大东区管理楼1218

报告摘要：

Fluid dynamics is essential for the many engineering designs including airplanes, car, kites, and turbines. However, their computational simulation and optimization are typically prohibitively expensive to make it happen in real-time inside the interactive design system. Leveraging the power of the recent development of the machine learning, we can drastically accelerate the aerodynamics computation. In this talk, I discuss the challenges in integrating CFD simulation into the 3D free-form shape design systems and introduce our approaches.

报告人简介: 

Nobuyuki Umetani is a research scientist at Autodesk Research. Previously, he was a postdoctoral researcher in Autodesk Research and Disney Research Zurich. He received his Ph.D. degree in 2012 from The University of Tokyo under the supervision of Takeo Igarashi. The principal research question he addresses through his studies is: how to integrate real-time physical simulation into interactive geometric modeling procedure to facilitate creativity. He is broadly interested in physics simulation, especially the finite element method, applied for computer animation, biomechanics, and mechanical engineering.

研究主页：https://autodeskresearch.com/people/nobuyuki-umetani