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 …

Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …

Sparse model identification enables the discovery of nonlinear dynamical systems purely
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …

This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …

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 …

PySINDy is a Python package for the discovery of governing dynamical systems models
from data. In particular, PySINDy provides tools for applying the sparse identification of …

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 …

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 …

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

A central challenge in data-driven model discovery is the presence of hidden, or latent,
variables that are not directly measured but are dynamically important. Takens' theorem …