讲座地点：ZOOM ID：567 306 5241 密 码：123456
报告摘要：Linear Multistep Methods (LMMs) are among the most widely used algorithms for numerically integrating dynamic systems. A comprehensive mathematical theory has been developed over the last century and has become textbook materials. Yet, there seems to be a new story when LMMs are used in a black box machine learning formulation for solving the inverse problem of discovering unknown dynamics from observation data. A natural question is concerned with whether a convergent LMMs for integrating dynamics is also suitable for dynamics discovery. We show in this lecture that the conventional theory of consistency, stability and convergence of LMM for time integration must be reexamined for dynamics discovery, which leads to new results on LMM that have never been given attention to in the past. We present refined concepts and algebraic criteria to assure stable and convergent discovery of dynamics in some idealized settings. We then apply the theory to some popular LMMs.