A Perspective on Machine Learning Methods in Turbulence Modelling

TL;DR

This paper reviews the current state and challenges of applying machine learning to turbulence modeling, emphasizing data consistency and physics integration, and surveys recent data-driven approaches in the field.

Contribution

It provides a comprehensive overview of ML methods in turbulence modeling, highlighting key challenges, recent developments, and the importance of data and physics consistency.

Findings

ML methods show promise for turbulence closure modeling

Data consistency and physics integration are critical for success

Survey of recent data-driven turbulence modeling approaches

Abstract

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 advantages and promises of machine learning methods applied to parameter estimation, model identification, closure term reconstruction and beyond, mostly from the perspective of Large Eddy Simulation and related techniques. We stress that consistency of the training data, the model, the underlying physics and the discretization is a key issue that needs to be considered for a successful ML-augmented modeling strategy. In order to make the discussion useful for non-experts in either field, we introduce both the modeling problem in turbulence as well as the prominent ML paradigms and methods in a concise and self-consistent manner. Following, we present a survey of the current data-driven model concepts and methods, highlight important developments and put them into the context of the discussed challenges.