This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

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 …

High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …

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

Modal-decomposition techniques are computational frameworks based on data aimed at
identifying a low-dimensional space for capturing dominant flow features: the so-called …

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 propose a customized convolutional neural network based autoencoder called a
hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow …

Recent applications of machine learning, in particular deep learning, motivate the need to
address the generalizability of the statistical inference approaches in physical sciences. In …

Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the mapping between …

We investigate the capability of machine learning (ML) based reduced order model (ML-
ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds …