Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

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

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

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 …

This work presents a review of the current state of research in data‐driven turbulence
closure modeling. It offers a perspective on the challenges and open issues but also on the …

The mechanical behavior of inelastic materials with microstructure is very complex and hard
to grasp with heuristic, empirical constitutive models. For this purpose, multiscale …

Variational autoencoder architectures have the potential to develop reduced-order models
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …