  In the field of phase change phenomena, the lack of accessible and diverse
datasets suitable for machine learning (ML) training poses a significant
challenge. Existing experimental datasets are often restricted, with limited
availability and sparse ground truth data, impeding our understanding of this
complex multiphysics phenomena. To bridge this gap, we present the BubbleML
Dataset
\footnote{\label{git_dataset}\url{https://github.com/HPCForge/BubbleML}} which
leverages physics-driven simulations to provide accurate ground truth
information for various boiling scenarios, encompassing nucleate pool boiling,
flow boiling, and sub-cooled boiling. This extensive dataset covers a wide
range of parameters, including varying gravity conditions, flow rates,
sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is
validated against experimental observations and trends, establishing it as an
invaluable resource for ML research. Furthermore, we showcase its potential to
facilitate exploration of diverse downstream tasks by introducing two
benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b)
operator networks for learning temperature dynamics. The BubbleML dataset and
its benchmarks serve as a catalyst for advancements in ML-driven research on
multiphysics phase change phenomena, enabling the development and comparison of
state-of-the-art techniques and models.
