  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.
