Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

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 …

THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

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

Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …

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 focus on a convolutional neural network (CNN), which has recently been utilized for fluid
flow analyses, from the perspective on the influence of various operations inside it by …

Sparse principal component analysis (SPCA) has emerged as a powerful technique for
modern data analysis, providing improved interpretation of low-rank structures by identifying …

We propose a universal method for data-driven modeling of complex nonlinear dynamics
from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics …