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This paper introduces BubbleSH, a high-fidelity dataset capturing the complex dynamics of rising bubbles in a fluid, derived from direct numerical simulations. It provides detailed time-resolved data on bubble trajectories, velocities, and morphological changes, represented through spherical harmonics, facilitating the study of bubble interactions and shape deformations. The dataset is evaluated using a probabilistic emulator, establishing a benchmark for data-driven modeling of chaotic multiphase systems, highlighting the sensitivity of bubble dynamics to local perturbations.
BubbleSH reveals that bubble-swarm dynamics are highly sensitive to local perturbations, offering a rich dataset for training generative models on future trajectory predictions.
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.
