报告题目：A Learning-based Projection Method for Model Order Reduction of Transport Problems
报告时间：2022年1月7日 (星期五) 上午9:00 - 10:30
腾讯会议：506-387-535
报告人：彭志超
邀请人：王素林
报告摘要：The Kolmogorov n-width of the solution manifolds of transport-dominated problems can decay slowly. As a result, it can be challenging to design efficient and accurate reduced order models (ROMs) for such problems. To address this issue, we propose a new learning-based projection method to construct nonlinear adaptive ROMs for transport problems. The construction follows the offline-online decomposition. In the offline stage, we train a neural network to construct adaptive reduced basis dependent on time and model parameters. In the online stage, we project the solution to the learned reduced manifold. Inheriting the merits from both deep learning and the projection method, the proposed method is more efficient than the conventional linear projection-based methods, and may reduce the generalization error of a solely learning-based ROM.
报告人简介：彭志超，博士，2015年本科毕业于北京大学，2020年博士毕业于Rensselaer Polytechnic Institute（伦斯勒理工学院），2020年至今在Michigan State University（密歇根州立大学）做博士后。目前主要研究动理学问题、波动方程和电磁学方程等的高阶高效保结构的数值计算方法。
