Data-driven modeling for drop size distributions

High-fidelity multiphase fluid dynamics data are scarce and expensive. We propose a Bayesian data-driven method to predict the drop size distribution of a liquid jet from sparse measurements. The proposed method provides uncertainty estimation, and it can be used for the optimal design of experiments.