Learning active particle dynamics and hydrodynamics from experimental data

Recent advances in live-imaging techniques enable accurate tracking of self-propelled colloids, swimming microorganisms, and other active particle systems across a wide range of length and time scales. This remarkable experimental progress presents an opportunity to infer quantitative models of complex particle-particle interactions and emergent collective dynamics directly from video microscopy data. In this talk, I will summarize our recent efforts to integrate spectral methods with deterministic and stochastic inference schemes to identify predictive ODE, SDE, and PDE models for various active matter systems. By focusing on examples from recent microfluidics (and other) experiments, we illustrate the promise of such approaches for simultaneously measuring multiple system parameters that may otherwise be difficult to access experimentally.