In fluid experiments, the temporal resolution of velocity fields is typically constrained by the complexity and high cost of measurement systems. Although recent studies have explored the temporal super-resolution of velocity fields using data-driven approaches, they typically rely on large amounts of high-frequency experimental data for supervised training, resulting in high costs and limited generalization capabilities. To overcome this bottleneck, we focus on cavitating hydrofoil flows and propose a semisupervised deep learning framework, DAIT-DNN (Domain-Adaptive Iterative Transfer Deep Neural Network), which completely eliminates the reliance on external high-frequency velocity datasets. The proposed method innovatively employs low-cost, nonintrusive high-speed imaging to capture high-frequency binary phase fields, which serve as inputs to enable temporal super-resolution reconstruction based on its own sparsely sampled, low-frequency velocity field data. The results demonstrate that, under sparse sampling conditions, DAIT-DNN—integrating pseudolabel assistance, domain adaptation, and an iterative optimization strategy—significantly improves the generalization capability of few-shot modeling compared to conventional DNN models. Particularly, for certain highly unsteady conditions or specific moments, DAIT-DNN trained on low-frequency data even outperforms traditional models trained on high-frequency datasets. By integrating low-cost phase information with few-shot velocity data, this work offers an efficient, economical solution for reconstructing complex cavitating flow fields and broadens the potential applications of deep learning to modeling complex physical systems.

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• Cavitation
• Multiphase flows
• Deep learning
• Machine learning