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

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

In terms of multiple temporal and spatial scales, massive data from experiments, flow field
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …

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 …

The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …

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 …

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 …

Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …

Machine-learning (ML) algorithms offer a new path for investigating high-dimensional,
nonlinear problems, such as flow-dynamical systems. The development of ML methods …