报告题目：Deep adaptive density approximation for Fokker-Planck type equations
报告人：曾莉 副教授 福州大学
报告时间：2023年 06月26日（星期一）10:00-11:00
报告地点: 正新楼201
校内联系人：王翔 wxjldx@jlu.edu.cn
报告摘要：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.
报告人简介：曾莉，2018年本科毕业于北京师范大学，数学与应用数学专业，2023年06月获得中国科学院数学与系统科学研究院博士学位，将于2023年08月入职福州大学副教授。
