Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks

The recent development of high-performance computing enables us to generate spatio-temporal high-resolution data of nonlinear dynamical systems and to analyze them for a deeper understanding of their complex nature. This trend can be found in a wide range of science and engineering, which suggests that detailed investigations on efficient data handling in physical science must be required in the future. This study considers the use of convolutional neural networks (CNNs) to achieve efficient data storage and estimation of scientific big data derived from nonlinear dynamical systems. The CNN is used to reconstruct three-dimensional data from a few numbers of two-dimensional sections in a computationally friendly manner. The present model is a combination of two- and three-dimensional CNNs, which allows users to save only some of the two-dimensional sections to reconstruct the volumetric data. As examples, we consider a flow around a square cylinder at the diameter-based Reynolds number $Re_D = 300$. We demonstrate that volumetric fluid flow data can be reconstructed with the present method from as few as five sections. Furthermore, we propose a combination of the present CNN-based reconstruction with an adaptive sampling-based super-resolution analysis to augment the data compressibility. Our report can serve as a bridge toward practical data handling for not only fluid mechanics but also a broad range of physical sciences.