295 citations · 535 across the 6 of their papers we have counts for
6 papers
Feedback Control of Turbulent Shear Flows by Genetic Programming
Thomas Duriez, Vladimir Parezanović, Kai von Krbek +14
Turbulent shear flows have triggered fundamental research in nonlinear dynamics, like transition scenarios, pattern formation and dynamical modeling. In particular, the control of…
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…
Closed-loop separation control using machine learning
Nicolas Gautier, Thomas Duriez, Jean-Luc Aider +3
A novel, model free, approach to experimental closed-loop flow control is implemented on a separated flow. Feedback control laws are generated using genetic programming where they…
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…
Attractor Control Using Machine Learning
Thomas Duriez, Vladimir Parezanovic, Bernd R. Noack +3
We propose a general strategy for feedback control design of complex dynamical systems exploiting the nonlinear mechanisms in a systematic unsupervised manner. These dynamical syst…
Cluster-based reduced-order modelling of a mixing layer
Eurika Kaiser, Bernd R. Noack, Laurent Cordier +7
We propose a novel cluster-based reduced-order modelling (CROM) strategy of unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt et al. 2006…