報告題目：Deep adaptive density approximation for Fokker-Planck type equations
報告人：曾莉 副教授 福州大學
報告時間：2023年 06月26日（星期一）10:00-11:00
報告地點: 正新樓201
校内聯系人：王翔 wxjldx@jlu.edu.cn
報告摘要：In recent years, deep learning algorithms based on deep neural networks have been widely applied to solving high-dimensional partial differential equations, which include physics-informed neural networks (PINNs), Deep Ritz method, and so on. In this talk, we start from Fokker-Planck equations and propose flow-based adaptive sampling strategies to improve the efficiency and accuracy of PINNs for solving partial differential equations whose solutions are probability density functions.
報告人簡介：曾莉，2018年本科畢業于北京師範大學，數學與應用數學專業，2023年06月獲得中國科學院數學與系統科學研究院博士學位，将于2023年08月入職福州大學副教授。
