No single flow reconstruction method works best everywhere—classical approaches dominate in well-constrained problems, but learned models become essential when sensors are sparse or poorly placed. Generative models offer better uncertainty quantification at higher computational cost.
FlowReconBench compares 15 methods (10 learned, 5 classical) for reconstructing complete fluid flow fields from sparse sensor data across three test scenarios. The study reveals that the best approach depends on the problem: classical methods excel with dense sensors and simple flows, while neural networks shine with sparse or poorly-positioned sensors.