Constructing New Statistical Physics Theories From Big Data

The glass transition is an infamous example of an emergent collective phenomenon in many-body systems that is stubbornly resistant to microscopic understanding. The transition combines three major theoretical challenges by involving nonlinear responses that occur far from equilibrium in disordered systems. Establishing the connection between microscopic properties and the glass transition requires reducing vast quantities of microscopic information to a few relevant microscopic variables and their distributions.

In this lecture, Andrea Liu will demonstrate how machine learning, designed for dimensional reduction, can provide a natural way forward when standard statistical physics tools fail. She will describe how she and her group have harnessed machine learning to identify an important microscopic quantity for the glass transition — a structural order parameter— from snapshot images. She will then explain how they use that quantity to build a new theory for glassy dynamics and how big data approaches can be applied to other statistical physics problems, including in living systems.

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On-site registration will not be permitted. Walk-in entry will be denied.