Graph-informed simulation-based inference for models of active matter

Many collective systems exist in nature far from equilibrium, ranging from
cellular sheets up to flocks of birds. These systems reflect a form of active
matter, whereby individual material components have internal energy. Under
specific parameter regimes, these active systems undergo phase transitions
whereby small fluctuations of single components can lead to global changes to
the rheology of the system. Simulations and methods from statistical physics
are typically used to understand and predict these phase transitions for
real-world observations. In this work, we demonstrate that simulation-based
inference can be used to robustly infer active matter parameters from system
observations. Moreover, we demonstrate that a small number (from one to three)
snapshots of the system can be used for parameter inference and that this
graph-informed approach outperforms typical metrics such as the average
velocity or mean square displacement of the system. Our work highlights that
high-level system information is contained within the relational structure of a
collective system and that this can be exploited to better couple models to
data.