Neural-Network-Assisted Boltzmann Approach for Dilute Microswimmer Suspensions

Abstract

We introduce a neural-network-assisted Boltzmann framework that learns the binary-collision map of microswimmers directly from data and uses it to evaluate collision integrals efficiently. Using a representative model swimmer, the learned map quantitatively predicts translational and rotational diffusivities and enables a linear-stability analysis of isotropy against polar ordering in dilute suspensions. The resulting predictions closely match direct simulations. The present framework is agnostic to active matter models and broadly applicable: once two-body collision data are obtained -- either from simulations or experiments -- the same surrogate can be used to evaluate kinetic transport across dilute conditions where binary collisions dominate. Because the workflow relies only on pre- and post-collision statistics, the present approach provides a general data-driven route linking particle-scale interactions to macroscopic transport and collective behavior in active suspensions.