Deployable deep learning for flow-field reconstruction: sparse measurements, physical credibility, and generalization

High-resolution flow fields are essential for understanding, predicting, and controlling fluid systems, yet they remain difficult to obtain from either experiments or high-fidelity simulations. Deep-learning-based flow-field reconstruction has therefore become an attractive route for recovering dense flow information from sparse sensors, low-resolution simulations, or incomplete measurements. Existing studies have demonstrated impressive reconstruction accuracy on canonical benchmark cases, but benchmark accuracy alone does not ensure engineering usability. This Mini Review reframes deep-learning-based flow-field reconstruction from a deployability-oriented perspective. Here, deployability denotes the usability of a reconstruction method under realistic engineering conditions, including physically constrained sensor placement, noisy or asynchronous measurements, operating states outside the training distribution, and the requirement that reconstructed outputs satisfy physical-consistency checks. Rather than organizing the literature only by network architecture, this review examines three coupled axes: sparse measurement realism, physical credibility, and generalization. Finally, we discuss operator learning, spectral bias, benchmark reproducibility, and propose a minimum pre-deployment validation checklist covering sensor robustness, measurement quality, physical residuals, spectral consistency, generalization, and derived engineering quantities.