  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 deeper understanding of their complex nature. This trend can
be found in a wide range of science and engineering communities, which suggests
that detailed investigations on efficient data handling in physical science
must be required in future. To this end, we introduce the use of convolutional
neural networks (CNNs) to achieve an efficient data storage and estimation of
scientific big data derived from nonlinear dynamical systems. The CNN is
utilized 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 an example of three-dimensional data, we consider a fluid flow around
a square cylinder at the diameter-based Reynolds number $Re_D$ of 300, and show
that volumetric fluid flow data can successfully be reconstructed with the
present method from as few as five sections. Furthermore, we also propose a
combination of the present CNN-based reconstruction with an adaptive
sampling-based super-resolution analysis to augment the data compression
capability of the present methods. Our report can be a significant bridge
toward practical data handling for not only the fluid mechanics field but also
a vast range of physical sciences.
