  Ocean dynamics constitute a source of incertitude in determining the ocean's
role in complex climatic phenomena. Current observation systems have
limitations in achieving sufficiently statistical precision for
three-dimensional oceanic data. It is crucial knowledge to describe the
behavior of internal ocean structures. We present the data-driven approaches
which explore latent class regressions and deep regression neural networks in
modeling ocean dynamics in the extensions of Gulf Stream and Kuroshio currents.
The obtained results show a promising data-driven direction for understanding
the ocean's characteristics, including salinity and temperature, in both
spatial and temporal dimensions in the turbulent regions. Our source codes are
publicly available at https://github.com/v18nguye/gulfstream-lrm and at
https://github.com/sagudelor/Kuroshio.
