Learning deterministic hydrodynamic equations from stochastic active particle dynamics
This enables discovery of multi-scale models for non-equilibrium stochastic processes in collective biological movement, though it appears incremental as it combines existing statistical learning with physical priors.
The researchers tackled the problem of learning deterministic hydrodynamic equations from stochastic active particle trajectories, developing a data-driven method that successfully inferred continuum models for density lanes in self-propelled particles and cell dynamics in epithelial tissues, while also identifying latent phoretic fields in chemotaxis.
We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems and to learning a continuum description of cell dynamics in epithelial tissues. We also infer from stochastic particle trajectories the latent phoretic fields driving chemotaxis. This demonstrates that statistical learning theory combined with physical priors can enable discovery of multi-scale models of non-equilibrium stochastic processes characteristic of collective movement in living systems.