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The recent development of high-performance computing enables us to generatespatio-temporal high-resolution data of nonlinear dynamical systems and toanalyze them for a deeper understanding of their complex nature. This trend canbe found in a wide range of science and engineering, which suggests thatdetailed investigations on efficient data handling in physical science must berequired in the future. This study considers the use of convolutional neuralnetworks (CNNs) to achieve efficient data storage and estimation of scientificbig data derived from nonlinear dynamical systems. The CNN is used toreconstruct three-dimensional data from a few numbers of two-dimensionalsections in a computationally friendly manner. The present model is acombination of two- and three-dimensional CNNs, which allows users to save onlysome of the two-dimensional sections to reconstruct the volumetric data. Asexamples, we consider a flow around a square cylinder at the diameter-basedReynolds number $Re_D = 300$. We demonstrate that volumetric fluid flow datacan be reconstructed with the present method from as few as five sections.Furthermore, we propose a combination of the present CNN-based reconstructionwith an adaptive sampling-based super-resolution analysis to augment the datacompressibility. Our report can serve as a bridge toward practical datahandling for not only fluid mechanics but also a broad range of physicalsciences.

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