Learning locally dominant force balances in active particle systems
This work addresses the problem of understanding self-organized patterns in active matter for physicists and computational scientists, but it is incremental as it applies existing clustering and inference methods to a known hydrodynamic model.
The researchers tackled the challenge of identifying local interactions that drive pattern formation in active particle systems, using data-driven analysis to reveal that propagating bands are formed by alignment interactions from density gradients and steady-state asters by splay-induced negative compressibility, with results showing excellent agreement with analytical and experimental data.
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.