Physics-Aware Heat Partitioning Network

Multimodal measurements are critical for understanding two-phase boiling heat transfer, yet experimentally resolving phase-specific heat flux contributions remains expensive and challenging. Building on well-established heat-partitioning formulas, we introduce PAPA-Net, a Physics-Aware Heat Partitioning Network for phase-only, mechanism-resolved wall heat flux prediction. PAPA-Net factorizes each heat transfer component into a soft physical support mask and a nonnegative heat-amplitude field, yielding dry area, microlayer, and single phase contributions that sum to the total prediction. Sparse teacher masks are used only during training to assign mechanism identity, while inference requires only optical observations. Physics-coupled objectives enforce consistency between local predictions, mechanism contributions, and integrated heat balance. Across training, validation and external zero-shot conditions, PAPA-Net maintains stable, noncollapsed physical partitions and, on the held-out condition, reduces integrated heat error by 20.4% relative to the next-best optical-only method. Ablations, label-efficiency, robustness, uncertainty, and controlled synthetic experiments further show that the mask-amplitude factorization yields mechanism-aligned heat flux partitions rather than a cosmetic latent decomposition.