Bubbleformer: Forecasting Boiling with Transformers
Sheikh Md Shakeel Hassan, Xianwei Zou, Akash Dhruv, Aparna Chandramowlishwaran
Modeling boiling--an inherently chaotic, multiphase process central to energy and thermal systems--remains a significant challenge for neural PDE surrogates. Existing models rely on simulation inputs (e.g., bubble positions), limiting their ability to forecast future states autonomously. We introduce Bubbleformer, a transformer-based spatiotemporal model that forecasts stable and long-range boiling dynamics including nucleation, interface evolution, and heat transfer without dependence on simulation data during inference. Bubbleformer combines axial attention with physics-informed architectural modifications to mitigate spectral bias and conditions on thermophysical parameters to generalize across fluids, geometries, and operating conditions. To evaluate models of these chaotic systems, we introduce physically grounded metrics. We also release BubbleML 2.0, a high-fidelity dataset spanning diverse working fluids (cryogens, refrigerants, and dielectrics), boiling configurations (pool boiling and flow boiling), distinct flow boiling regimes (bubbly, slug, and annular), and operational and boundary conditions. Bubbleformer sets new benchmarks in both prediction and forecasting tasks of two-phase boiling flows.