FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements

The recent study on FLRONet addresses the complex challenge of reconstructing high-fidelity fluid flow fields from sparse sensor measurements, a task crucial for various scientific and engineering applications. This research is significant as it tackles the issues arising from dimensional disparities between state and observational spaces, which often lead to ill-conditioned and non-invertible measurement operators. By improving the reconstruction process, this work could enhance the accuracy and efficiency of fluid dynamics modeling, benefiting multiple industries.