Constructing New Statistical Physics Theories From Big Data
Andrea Liu, Ph.D.University of Pennsylvania
Simons Foundation Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.
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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