Learning locally dominant force balances in active particle systems

TL;DR

This paper introduces a data-driven approach combining clustering and inference algorithms to identify local force balances that explain pattern formation in active particle systems, bridging microscopic interactions and macroscopic structures.

Contribution

It presents a novel method for uncovering local physical mechanisms in active matter, validated across different models and consistent with experimental data.

Findings

Propagating bands are driven by local alignment interactions.

Steady-state asters are shaped by splay-induced negative compressibility.

The method reveals common physical principles across models.

Abstract

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate, and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, like asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.