報告題目：Two novel deep neural networks methods for high dimensional PDEs
報 告 人：鄒青松 教授 中山大學
報告時間：2022年7月8日 星期五 9:00-10:00
報告平台：騰訊會議 ID: 169-740-404
會議聯系人：王翔 wxjldx@jlu.edu.cn
報告摘要：In this talk, we will report two new deep NN methods for high order PDEs. The first one is the so-called adaptive neural networks method (ADN). By applying three adaptive techniques: adaptive activation function, adaptive loss and adaptive sampling, to the well-known DNN method PINN, we significantly improve the accuracy of the PINN method. Our second method is the so-called deep temporal difference methods (DTD). With this method, we first transform the deterministic parabolic PDE to a system of forward backward stochastic differential equation. Then by regarding this FBSDE as a Markov rewarding process, we use the Temporal Difference method in the reinforcement learning to train a neural network. Comparing to the deep stochastic method such as deep BSDE in the literature, our method can improve the accuracy and computational speed.
報告人簡介：鄒青松，中山大學計算機學院教授，博士生導師，數據科學系主任，廣東省計算數學學會理事長,期刊International Journal of Numerical Analysis and Modelling編委。長期從事偏微分方程數值解法方面的研究工作，在包括SIAM J Numer Anal, Math Comp, Numer Math等在内的知名國際發表論文60餘篇。 主要研究方向包括高階高精度有限體積法（項目“高次有限體積法的構造和理論分析”獲得2020年廣東省自然科學獎二等獎），偏微分方程深度學習算法，以及藥物設計和金融工程中的人工智能算法等。
