Machine learning models for prediction of droplet collision outcomes

TL;DR

This paper demonstrates that machine learning classifiers, trained on a large dataset and incorporating physics knowledge, can predict droplet collision outcomes with over 90% accuracy, outperforming traditional physics-based models.

Contribution

The study introduces a large labeled dataset and evaluates ML classifiers, showing their superior accuracy and robustness in predicting droplet collision outcomes.

Findings

ML classifiers achieve over 90% accuracy.

Physics knowledge marginally improves small dataset performance.

Traditional models have about 43% accuracy.

Abstract

Predicting the outcome of liquid droplet collisions is an extensively studied phenomenon but the current physics based models for predicting the outcomes are poor (accuracy $\approx 43\%$). The key weakness of these models is their limited complexity. They only account for 3 features while there are many more relevant features that go unaccounted for. This limitation of traditional models can be easily overcome through machine learning modeling of the problem. In an ML setting this problem directly translates to a classification problem with 4 classes. Here we compile a large labelled dataset and tune different ML classifiers over this dataset. We evaluate the accuracy and robustness of the classifiers. ML classifiers, with accuracies over 90\%, significantly outperform the physics based models. Another key question we try to answer in this paper is whether existing knowledge of the physics based models can be exploited to boost the accuracy of the ML classifiers. We find that while this knowledge improves the accuracy marginally for small datasets, it does not improve accuracy with if larger datasets are used for training the models.