BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces
For researchers in multiphase flow and machine learning, this dataset enables data-driven modeling of deformable bubble interactions, offering a compact high-fidelity benchmark for generative models.
The paper introduces BubbleSH, a dataset of 3D bubble swarm dynamics from direct numerical simulations, providing trajectories, velocities, and shape evolution via spherical harmonics. It establishes a benchmark for data-driven models, evaluating an equivariant probabilistic emulator on trajectory and shape prediction tasks.
Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological behavior. We present BubbleSH, a bubbly flows dataset consisting of transient, three-dimensional bubble-swarm dynamics obtained from high-fidelity direct numerical simulations of bubbles rising in a periodic domain. The dataset provides time-resolved bubble trajectories, velocities, and shape evolution, with bubble morphology compactly represented using spherical harmonics. Designed to be lightweight yet physically expressive, the dataset enables data-driven modeling of bubbly flow simulators where shape deformation and bubble-bubble interactions play a central role. We characterize the dataset with bubble kinematics, morphology, and interaction patterns, and introduce evaluation metrics for both trajectory and shape prediction. The sensitivity of bubble-swarm dynamics to local perturbations makes BubbleSH particularly well suited to generative models that learn distributions over possible future trajectories. We evaluate a permutationally and translationally equivariant probabilistic emulator on BubbleSH given the proposed metrics. Therefore, we establish a compact, high-fidelity dataset and a benchmark for developing and evaluating data-driven models of deformable, chaotic multiphase systems.