Focus and expertize

Our research is focussed on closed-loop flow control for transport systems demonstrated in real-world experiments. We address key challenges of turbulence control from the inherent nonlinearity of actuation mechanism to noise in experiments.

Our control design strategy consists of two steps: exploration and exploitation. The model-free exploration identifies the optimal nonlinear actuation mechanism in an automatic (unsupervised) self-learning manner. This approach employs machine learning control (MLC).

The fine-tuning (exploitation) of the optimal actuation law is performed in a model-based control design. This step requires on-line capable reduced order models (ROM).

While enjoying a large spectrum of mathematical methods and physical theories, our ultimate pleasure comes from a control strategy working successfully in experiment. Current projects include mixing-layer manipulation, separation control, drag reduction of bluff bodies, lift increase of airfoils and jet noise reduction.


TUCOROM team (subset) at the control panel of the TUCOROM mixing layer demonstrator. Top row (left to right): Steven BRUNTON, Vladimir PAREZANOVIC, Jean-Charles LAURENTIE; Bottom row (left to right): Marc SEGOND, Thomas DURIEZ, Bernd NOACK.

Books and review articles

Data-Driven Fluid Mechanics Duriez, Brunton & Noack 2016 Springer Noack , Morzynski & Tadmor  2011 Springer
  • Brunton, S. L., Noack, B. R. & Koumoutsakos, P. (2020) "Machine learning for fluid mechanics". Annual Reviews of Fluid Mechanics 52., 477--508.
  • Brunton, S. L. & Noack, B. R. (2015) "Closed-loop turbulence control: Progress and challenges". Applied Mechanics Reviews 67(5), article 050801, 1-48.
    (html) (pdf) (BibTeX)
  • Mendez, M. A., Ianiro, A., Noack, B. R. & Brunton, S. L. (2023) "Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning". Cambridge University Press. (html)
  • Duriez, T., Brunton, S. L. & Noack, B. R. (2017) "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence." Series 'Fluid Mechanics and Its Applications 116, Springer-Verlag.
    (html) Powered by OpenMLC.
  • Noack, B. R. Morzynski, M. & Tadmor, G.(eds.) (2011) "Reduced-Order Modelling for Flow Control." Series 'CISM Courses and Lectures' 528, Springer-Verlag, Vienna.
  • Cornejo Maceda, G. Y., Lusseyran, F. & Noack, B. R. (2022) "xMLC---A Toolkit for Machine Learning Control" Series 'Machine Learning Tools for Fluid Mechanics' 2, Technische Universität Braunschweig, Germany.
    (PDF + Software)
  • Semaan, R., Fernex, D., Weiner, A. & Noack, B.R. (2011) "xROM --- A Toolkit for Reduced-Order Modeling of Fluid Flows." Series 'Machine Learning Tools for Fluid Mechanics' 1, Universitätsbibliothek der Technischen Universität Braunschweig, Germany.
    (html) Powered by xROM.