Learning hydrodynamic equations for active matter from particle simulations and experiments

Understand

Recent advances in high-resolution imaging techniques and particle-based simulation methods have enabled the precise microscopic characterization of collective dynamics in various biological and engineered active matter systems.

• In parallel, data-driven algorithms for learning interpretable continuum models have shown promising potential for the recovery of underlying partial differential equations (PDEs) from continuum simulation data.
• By contrast, learning macroscopic hydrodynamic equations for active matter directly from experiments or particle simulations remains a major challenge.
• Here, we present a framework that leverages spectral basis representations and sparse regression algorithms to discover PDE models from microscopic simulation and experimental data, while incorporating the relevant physical symmetries.