汇报标题 (Title)：Deep Adaptive Sampling and its Application on Surrogates（深度自适应采样及其在代理模型中的利用）
汇报人 (Speaker)：翟佳羽 助理教授（上？萍即笱В
汇报功夫 (Time)：2025年6月13日（周五）11:00
汇报地址 (Place)：宝山校区GJ303
约请人(Inviter)：纪丽洁
主办部门：理学院数学系
提要：We present two deep adaptive sampling methods and apply one to surrogate modeling of low-regularity parametric differential equations and illustrate that this mechanism is necessary for constructing surrogate models, to deal with the sampling problem in high dimensional parameter and physics spaces, with a relatively small sample size. Both the surrogate model and sampling model are approximated with deep neural networks. In particular, the sampling model is a normalizing flow, so that the sampling is immediate. We demonstrate the effectiveness of the proposed method with a series of numerical experiments.
