AI-enabled Differentiable Methods for Computer-aided Design and Engineering

发布时间:2023年12月26日 作者:王小捷   阅读次数:[]

报告题目:AI-enabled Differentiable Methods for Computer-aided Design and Engineering




报告摘要:The integration of computer-aided design (CAD) and computer-aided engineering (CAE) requires solving partial differential equations (PDEs) over complex geometries and optimizing quantities of interest with respect to the geometric parameterization under physical constraints. In this talk, we will delve into artificial intelligence (AI)-enabled differentiable methods in CAD and CAE, which have the potential to address these challenges. In particular, we will introduce: (1) the random feature method for solving PDEs, which promises to be a robust method in terms of accuracy and geometric complexity; (2) AI-enabled differentiable method, which automates the structure optimization process with little manual intervention.

个人简介:Jingrun Chen is currently a professor at the School of Mathematical Sciences and the Suzhou Advanced Research Institute at the University of Science and Technology of China, specializing in scientific computing and artificial intelligence. He has published over 50 academic papers in related fields. His research was supported by the National Natural Science Foundation of China, the National Key R&D Program of China (国家重点研发计划), the Overseas High-level Youth Talent Plan (国家海外高层次人才青年项目), and the National Science Foundation of USA.

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