This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

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

To find the path that minimizes the time to navigate between two given points in a fluid flow
is known as Zermelo's problem. Here, we investigate it by using a Reinforcement Learning …

Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …

Nudging is an important data assimilation technique where partial field measurements are
used to control the evolution of a dynamical system and/or to reconstruct the entire phase …

We study the applicability of tools developed by the computer vision community for feature
learning and semantic image inpainting to perform data reconstruction of fluid turbulence …

Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical
flows but often require a set of ensemble forward simulations to estimate forecast …

Motivated by recent progress in data assimilation, we develop an algorithm to dynamically
learn the parameters of a chaotic system from partial observations. Under reasonable …

We study a continuous data assimilation algorithm proposed by Azouani, Olson, and Titi
(AOT) in the context of an unknown viscosity. We determine the large-time error between the …