Super-resolution analysis via machine learning: a survey for fluid flows
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 …
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
Super-resolution reconstruction of turbulent flows with machine learning
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 …
turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning …
Assessment of supervised machine learning methods for fluid flows
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 …
fluid dynamics. Four machine learning architectures are examined in terms of their …
Zermelo's problem: optimal point-to-point navigation in 2D turbulent flows using reinforcement learning
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 …
is known as Zermelo's problem. Here, we investigate it by using a Reinforcement Learning …
Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks
Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
Synchronization to big data: Nudging the Navier-Stokes equations for data assimilation of turbulent flows
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 …
used to control the evolution of a dynamical system and/or to reconstruct the entire phase …
Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database
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 …
learning and semantic image inpainting to perform data reconstruction of fluid turbulence …
[HTML][HTML] Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
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 …
flows but often require a set of ensemble forward simulations to estimate forecast …
Dynamically learning the parameters of a chaotic system using partial observations
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 …
learn the parameters of a chaotic system from partial observations. Under reasonable …
Parameter recovery for the 2 dimensional Navier--Stokes equations via continuous data assimilation
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 …
(AOT) in the context of an unknown viscosity. We determine the large-time error between the …