Applications of Machine Learning in the Mathematical Physics of Polymers
发布时间：2019年01月08日
浏览次数：5717
发布者: Xiaoni Tan
主讲人： Jeff Z. Y. Chen (University of Waterloo)
活动时间： 从 2019-01-08 10:30 到 11:30
场地： Room 77201, Jingchunyuan 78, BICMR
A feed-forward neural network has a remarkable property which allows the network itself to be a universal approximator for any functions. Here we present a universal, machine-learning based solver for multi-variable partial differential equations. The algorithm approximates the target functions by neural networks and adjusts the network parameters to approximate the desirable solutions. The idea can be easily adopted for dealing with multi-variable, coupled integrodifferential equations, such as those in the self-consistent field theory of predicting polymer microphase- separated structures.
