Data driven method to learn the stochastic dynamical systems and its application in polymer dynamics

主讲人：陈小丽

摘要：In this talk, I will discuss how to use machine learning method to learn the stochastic problem. To begin, I will introduce the how to combine physics informed neural network(PINN) method and the sample observation data to learn the stochastic differential equation driven by Brown and Levy noise. Second, I will introduce how to use stochatic OnsagerNet to learn closure dynamical systems. We propose a general machine learning approach to construct reduced models for noisy, dissipative dynamics based on the Onsager principle for non-equilibrium systems.  Then I will demonstrate our method by modelling the folding and unfolding of a long polymer chain in an external field - a classical problem in polymer rheology - though our model is suitable for the description of a wide array of complex, dissipative dynamical systems arising in scientific and technological applications.

主讲人简介：陈小丽，中国地质大学特任教授。2020年博士毕业于华中科技大学，2018年9月至2020年8月在美国布朗大学进行联合培养。之后在新加坡国立大学数学系和功能智能材料研究院(I-FIM)做博士后研究。主要从事随机动力系统, 机器学习与动力系统的研究。已在SIAM Journal on Scientific Computing, Nature Computational Science, Computer Methods in Applied Mechanics and Engineering, Physica D等期刊发表多篇学术论文。

邀请人：李东方

时间：2024年5月28日（星期二）16:00-18:00