Machine Learning and Dynamical Systems

Mathematical modeling of dynamical systems (DS) is a central goal of the quantitative sciences. Although machine learning (ML) technologies are modern inventions, the interaction of data and dynamics has a long history. One of data science's first forays into modeling dynamics perhaps began with astronomy -- particularly with Ptolemy's archaic but instructive geocentric model of the cosmos, which culminated in Kepler's laws of planetary motion. These laws ultimately laid the empirical basis for Newton's landmark contributions. Since then, the interactions of DS and data science have matured in both breadth and depth.