Inferring activity from the flow field around active colloidal particles using deep learning

TL;DR

This paper demonstrates a deep learning approach to infer the activity and characteristics of active colloidal particles from their flow fields, enabling accurate predictions in multi-particle systems.

Contribution

Introduces a novel deep learning method to infer activity modes and particle parameters from flow fields of active colloids, applicable to multiple particles.

Findings

High accuracy in predicting activity modes and particle parameters.

Effective extension from single to many-particle systems.

Provides a principled framework for activity inference from flow data.

Abstract

Active colloidal particles create flow around them due to non-equilibrium process on their surfaces. In this paper, we infer the activity of such colloidal particles from the flow field created by them via deep learning. We first explain our method for one active particle, inferring the $2s$ mode (or the stresslet) and the $3t$ mode (or the source dipole) from the flow field data, along with the position and orientation of the particle. We then apply the method to a system of many active particles. We find excellent agreements between the predictions and the true values of activity. Our method presents a principled way to predict arbitrary activity from the flow field created by active particles.