16 citations · 21 across the 6 of their papers we have counts for
6 papers
xMLC -- A Toolkit for Machine Learning Control
Guy Y. Cornejo Maceda, François Lusseyran, Bernd R. Noack
xMLC is the second book of this `Machine Learning Tools in Fluid Mechanics' Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, p…
A fast converging particle swarm optimization through targeted, position-mutated, elitism (PSO-TPME)
Tamir Shaqarin, Bernd R. Noack
We dramatically improve convergence speed and global exploration capabilities of particle swarm optimization (PSO) through a targeted position-mutated elitism (PSO-TPME). The three…
Closed-loop control of an experimental mixing layer using machine learning control
Vladimir Parezanović, Thomas Duriez, Laurent Cordier +6
A novel framework for closed-loop control of turbulent flows is tested in an experimental mixing layer flow. This framework, called Machine Learning Control (MLC), provides a model…
Control Volume Analysis, Entropy Balance and the Entropy Production in Flow Systems
Robert K. Niven, Bernd R. Noack
This chapter concerns "control volume analysis", the standard engineering tool for the analysis of flow systems, and its application to entropy balance calculations. Firstly, the p…
Closed-Loop Turbulence Control Using Machine Learning
Thomas Duriez, Vladimir Parezanović, Laurent Cordier +5
We propose a general model-free strategy for feedback control design of turbulent flows. This strategy called 'machine learning control' (MLC) is capable of exploiting nonlinear me…
Discussion of Di Vita, A., On information thermodynamics and scale invariance in fluid dynamics, Journal of Thermodynamics & Catalysis 3: e108 (2012)
Robert K. Niven, Bernd R. Noack
We respond to two sets of criticisms of our analysis in Noack & Niven, Journal of Fluid Mechanics 700, 187--213 (2012), made by Di Vita, Journal of Thermodynamics & Catalysis, 3: e…