Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

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 …

Over the last years, supervised learning (SL) has established itself as the state-of-the-art for
data-driven turbulence modeling. In the SL paradigm, models are trained based on a …

Reinforcement learning (RL) is highly suitable for devising control strategies in the context of
dynamical systems. A prominent instance of such a dynamical system is the system of …

In this paper, a novel random forest (RF)-based multifidelity machine learning (ML) algorithm
to predict the high-fidelity Reynolds-averaged Navier-Stokes (RANS) flow field is proposed …

The amalgamation of machine learning algorithms (ML) with computational fluid dynamics
(CFD) represents a promising frontier for the advancement of fluid dynamics research …

Liquid metals play a central role in new generation liquid metal cooled nuclear reactors, for
which numerical investigations require the use of appropriate thermal turbulence models for …

The authors present generalized finite-volume-based discretized loss functions integrated
into pressure-linked algorithms for physics-based unsupervised training of neural networks …

Fluid dynamics of liquid metals plays a central role in new generation liquid metal cooled
nuclear reactors, for which numerical investigations require the use of an appropriate …

Modern high-order discretizations bear considerable potential for the exascale era due to
their high fidelity and the high, local computational load that allows for computational …