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 …

Deep learning technique, has made tremendous progress in fluid mechanics in recent
years, because of its mighty feature extraction capacity from complicated and massive fluid …

In the last 50 years there has been a tremendous progress in solving numerically the Navier-
Stokes equations using finite differences, finite elements, spectral, and even meshless …

Electroconvection is a multiphysics problem involving coupling of the flow field with the
electric field as well as the cation and anion concentration fields. Here, we use …

Two deep learning (DL) models addressing the super-resolution (SR) reconstruction of
turbulent flows from low-resolution coarse flow field data are developed. One is the static …

A general super-resolution reconstruction strategy was proposed for turbulent velocity fields
using a generative adversarial network-based artificial intelligence framework. Two …

Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly
critical as neural networks (NNs) are being widely adopted in addressing complex problems …

A wide range of problems in applied physics and engineering involve learning physical
displacement fields from data. In this paper we propose a deep neural network-based …

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 …

In this study, a deep learning-based approach is applied with the aim of reconstructing high-
resolution turbulent flow fields using minimal flow field data. A multi-scale enhanced super …