2024
DOI: 10.1063/5.0179132
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Low-dimensional representation of intermittent geophysical turbulence with high-order statistics-informed neural networks (H-SiNN)

R. Foldes,
E. Camporeale,
R. Marino

Abstract: We present a novel machine learning approach to reduce the dimensionality of state variables in stratified turbulent flows governed by the Navier–Stokes equations in the Boussinesq approximation. The aim of the new method is to perform an accurate reconstruction of the temperature and the three-dimensional velocity of geophysical turbulent flows developing non-homogeneities, starting from a low-dimensional representation in latent space, yet conserving important information about non-Gaussian structures captur… Show more

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