Speaker
Description
Understanding the full parameter dependence of microscopic structure
in active matter remains a central challenge, particularly for strong activity
and high density, where simulations become increasingly expensive. Here,
we present a data-driven approach that learns radial and angular correla-
tions in terms of the pair-correlation function g$(r,\theta)$ of passive and active
Brownian particles. Our predictions are in close quantitative agreement with
Brownian dynamics simulations, even for parameter values that the neural
networks had not previously encountered during training. Our predictions
are subsequently distilled into compact, closed-form expressions using sym-
bolic regression, providing an interpretable description of the underlying
structure. Our approach offers a unified and computationally efficient route
to understanding non-equilibrium correlations.
