A data-driven statistical description for the hydrodynamics of active matter

Modeling living systems at the collective scale can be very challenging because individual constituents themselves complex and respective interactions between may not fully understood. With advent of high throughput experiments in age big data, data-driven methods are on rise to overcome these challenges. Although machine-learning approaches help quantify correlations various players, they do directly shed light underlying physical principles such systems. Here, we present a method for obtaining phase-space density active matter that solution stochastic dynamic equation readily emerges, from which time space dependence order parameters extracted. If system is near steady state, illuminate how construct field theory subsequently make predictions about system. The first developed analytically calibrated using simulated data. then applied an experimental particles actively driven by Serratia marcescens bacterial swarm presence localized UV light. analysis demonstrates steady-state before sometime after obey Gaussian with spatially-varying 'mass' those regimes. This novel yet simple finding surprising given dynamics swarm. In response light, demonstrate there net flow away entropy increases We conclude discussion additional potential applications our as when internal structure dynamically changes result modified governing