Flow Reconstruction

Flow reconstruction aims to recover complete fluid flow fields from limited, often noisy, sensor measurements. Current research heavily utilizes deep learning, employing convolutional neural networks, diffusion models, and Gaussian process regression with physics-informed kernels to achieve this, often incorporating techniques like conformal mapping to improve generalization across different flow geometries. These advancements are crucial for improving simulations in various fields, from aerospace engineering and environmental science to biomedical applications where obtaining complete flow data is challenging or impractical. The resulting improved accuracy and efficiency of flow simulations have significant implications for scientific understanding and engineering design.