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 operator network (DeepONet) has demonstrated great success in various learning
tasks, including learning solution operators of partial differential equations. In particular, it …

This paper investigates deep learning methods in the framework of convolutional neural
networks for reconstructing compressible turbulent flow fields. The aim is to develop …

Data center thermal management requires a good understanding of critical cooling airflow
path. While CFD modeling excels at portraying airflow and temperature fields, it is often …

This study proposes a convolutional neural network (CNN) that enhances the resolution of
instantaneous snapshots of three-dimensional air temperature and wind velocity fields …

The advent of Exascale computing invites an assessment of existing best practices for
developing application readiness on the world's largest supercomputers. This work details …

Computational fluid dynamics is a cornerstone of the modern aerospace industry, providing
important insights through aerodynamic analysis while reducing the need for expensive …

Upscaling flow features from coarse-grained data is paramount for extensively utilizing
computational physics methods across complex flow, acoustics, and aeroelastic …

Deep Learning (DL) algorithms are becoming increasingly popular for the reconstruction of
high-resolution turbulent flows (aka super-resolution). However, current DL approaches …

Effusion cooling was one of the most important external cooling technologies for airfoils in
gas turbines and aeroengines. Due to the complicated flow field of effusion cooling …