A data-driven statistical field theory of active matter

One of the major challenges in modeling biological systems is that some of the key players and their interactions are not fully understood. With the advent of high throughput experiments, and in the age of big data, data-driven methods are on the rise. However, although machine-learning approaches have been useful so far, they do not necessarily shed light on the underlying principles of such systems. To begin to illuminate the underlying principles, I will present a data-driven statistical field theory for active matter---a leading candidate for quantifying living systems. The method is first developed analytically. The parameters of the model will be learned from observations of the active matter system of interest. I then validate the approach with several simulations.