Inferring Surface Slip in Active Colloids from Flow Fields Using Physics-Informed Neural Networks

TL;DR

This paper presents a physics-informed neural network approach to infer surface slip distributions of active colloids from flow field measurements, enabling better understanding of their propulsion mechanisms.

Contribution

The authors develop a neural network framework that combines flow data with physical laws to accurately reconstruct slip distributions and flow fields around active colloids.

Findings

Quantitative agreement with analytical and BEM solutions.

Successful reconstruction of slip even without near-particle flow data.

Framework applicable in both unbounded and confined geometries.

Abstract

The directed motion of active colloids is governed by spatial variations in surface chemistry and interfacial stress, yet these properties remain extremely difficult to measure directly. We introduce a physics-informed neural network framework that infers the slip distribution driving propulsion from partial observations of the surrounding flow. By combining sparse fluid velocity measurements with the Stokes equations and boundary constraints, the method reconstructs both the near-surface slip and the full velocity and pressure fields. Validation against analytical solutions and Boundary Element Method calculations for canonical active colloid models shows quantitative agreement in both unbounded and confined geometries. Crucially, the framework recovers the surface slip even when no flow data are available near the particle, demonstrating that accessible bulk measurements encode the interfacial stresses responsible for active motion. These results establish physics-informed inference as a powerful tool for characterizing and ultimately controlling interfacially driven transport in colloidal active matter.