Minimal droplet shape representation in experimental microfluidics using Fourier series and autoencoders

TL;DR

This paper presents a reversible method combining Fourier series and autoencoders to efficiently represent droplet shapes in microfluidics, reducing dimensionality from 8D to 2D for improved analysis and control.

Contribution

The novel approach integrates domain knowledge with autoencoders to significantly lower the shape representation dimensionality in microfluidics.

Findings

Reduced droplet shape representation to 2D from 8D

Enhanced accuracy in droplet shape analysis

Enabled applications like automated droplet generation

Abstract

We introduce a two-step, fully reversible process designed to project the outer shape of a generic droplet onto a lower-dimensional space. The initial step involves representing the droplet's shape as a Fourier series. Subsequently, the Fourier coefficients are reduced to lower-dimensional vectors by using autoencoder models. The exploitation of the domain knowledge of the droplet shapes allows us to map generic droplet shapes to just 2D space in contrast to previous direct methods involving autoencoders that could map it on minimum 8D space. This 6D reduction in the dimensionality of the droplet's description opens new possibilities for applications, such as automated droplet generation via reinforcement learning, the analysis of droplet shape evolution dynamics and the prediction of droplet breakup. Our findings underscore the benefits of incorporating domain knowledge into autoencoder models, highlighting the potential for increased accuracy in various other scientific disciplines.