Computational methods in fluid research have been progressing during the past few years,
driven by the incorporation of massive amounts of data, either in textual or graphical form …

The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

Machine learning (ie, modern data-driven optimization and applied regression) is a rapidly
growing field of research that is having a profound impact across many fields of science and …

A perspective is presented on how machine learning (ML), with its burgeoning popularity
and the increasing availability of portable implementations, might advance fluid mechanics …

The renewed interest from the scientific community in machine learning (ML) is opening
many new areas of research. Here we focus on trends in ML that are providing opportunities …

This paper provides a short overview of how to use machine learning to build data-driven
models in fluid mechanics. The process of machine learning is broken down into five …

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

Recent progress in machine learning and big data not only forms a new research paradigm,
but also provides opportunity to solve grand challenges in fluid mechanics. Following the …

Scientific machine learning (SciML) has advanced recently across many different areas in
computational science and engineering. The objective is to integrate data and physics …

Symbolic regression techniques are constantly gaining ground in materials informatics as
the machine learning counterpart capable of providing analytical equations exclusively …