Collective phenomena in non-equilibrium systems
Suppose someone gave you a terabyte of data on an epidemic. What are the theoretical concepts you need to know to understand collective behavior in such a system? In this lecture, we will take an interdisciplinary perspective on how order emerges in non-equilibrium systems. We will introduce the theoretical concepts we need to know to identify and understand collective order in space and time. We will begin by introducing core concepts from non-equilibrium statistical physics, such as field theory, renormalization group theory, and non-equilibrium phase transitions. We will ask how order emerges in and out of thermal equilibrium and how this changes in systems with disorder. We will complement these theoretical insights with approaches from data science and machine learning that allow identifying collective degrees of freedom in big data. We will synthesize these lessons using examples from ongoing research in biophysics.