BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning

TL;DR

BubbleML provides a comprehensive, physics-based simulation dataset for machine learning research on phase change phenomena, enabling new benchmarks for bubble dynamics and temperature modeling.

Contribution

The paper introduces BubbleML, a large-scale, validated simulation dataset for multiphysics boiling phenomena, along with benchmarks for optical flow and temperature dynamics learning.

Findings

Dataset covers diverse boiling scenarios with 79 simulations.

Validated against experimental data, ensuring accuracy.

Benchmarks demonstrate potential for ML model development.

Abstract

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.