“数理论坛”第114期：Data Science Plus Dynamical Systems: What Can We Learn?

报告题目

Data Science Plus Dynamical Systems: What Can We Learn?

报告时间

2019年10月16日（周三）下午16:30—17:30

报告地点

东区综合楼A座1404室

报告人

段金桥 教授（华中科技大学数学中心主任）

报告人

简介

段金桥教授是武汉大学学士，中国科学院硕士，美国康乃尔大学博士，和美国加州理工学院博士后。段金桥教授的研究领域包括数据科学、随机动力系统、非线性动力系统、随机偏微分方程以及数学与其它学科的交叉研究(地球科学、生命科学等有关的随机现象与复杂现象)。段金桥教授在非高斯随机动力系统，随机偏微分方程齐性化及其相关应用研究领域作出了重要贡献，并获得多项科研基金和科研奖励。他现任StochasticsandDynamics(“随机动力系统”)杂志管理编辑。他还任InterdisciplinaryMathematicalSciences(“跨学科应用数学丛书”)主编，以及“NonlinearProcessesinGeophysics”编委。

报告摘要

Observational datasets are abundant. Dynamical systems are mathematical models in engineering, medicine and science. Data are noisy and dynamical systems are often under random fluctuations (either Gaussian or non-Gaussian noise).

The interactions between data science and dynamical systems are becoming exciting. On the one hand, dynamical systems tools are valuable to extract information from datasets. On the other hand, data science techniques are indispensable for understanding dynamical behaviors with observational data.

I will present recent progress on extracting information like the most probable transition pathways, mean residence time, and escape probability from datasets, and on estimating system states and parameters with help of datasets. In addition to highlighting the underlying dynamical systems structures, such as stochastic flows, slow manifolds and dimension reduction, I will outline several mathematical issues at the foundation of relevant machine learning approaches.

邀请人

易鸣 教授

2019年 10月 14 日