Multiscale Modeling and Computation Seminar by Ming Zhong: Learning Self Organization from Observation (III)

Speaker: Ming Zhong, assistant professor of applied mathematics, Illinois Institute of Technology

Title: Learning Self Organization from Observation (III)

Abstract: Self-Organization (aka collective behavior) can be used to explain crystal formation, aggregation of cells, social behaviors of insects, synchronization of heart beats, etc. It is a challenging task to understand these types of phenomena from the mathematical point of view. We offer a statistical/machine learning approach to understand these behaviors from observation; moreover, our learning approach can aid in validating and improving the modeling of Self-Organization.

In the first part of the talk, we will focus on the forward modeling and backward learning of self organization.  We will review several important models which produce clustering, flocking, milling, and synchronization.  Then we will derive the learning method for inferring the interaction kernel from observation data and discuss its convergent properties.

In the second part of the talk, we will discuss how to expand the learning method to include more complicated models, complex geometries, missing feature variables, and how to handle real world data and observation noise.  We will also show a demo of the software suite for modeling and learning of self organization.

 

Multiscale Modeling and Computation