With the availability of petabytes of oceanographic observations and numerical model
simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of …

We use machine learning to perform super-resolution analysis of grossly under-resolved
turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning …

We present a new data reconstruction method with supervised machine learning techniques
inspired by super resolution and inbetweening to recover high-resolution turbulent flows …

We present a new nonlinear mode decomposition method to visualize decomposed flow
fields, named the mode decomposing convolutional neural network autoencoder (MD-CNN …

A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …

A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …

We apply supervised machine learning techniques to a number of regression problems in
fluid dynamics. Four machine learning architectures are examined in terms of their …

We propose a method to construct a reduced order model with machine learning for
unsteady flows. The present machine-learned reduced order model (ML-ROM) is …

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …