Bernd R. Noack
Also known as Bernd Noack
Bernd R. Noack is a physicist whose work applies machine learning to turbulence and fluid-flow control. He earned physics degrees in Göttingen and held academic roles in Berlin, France, and Braunschweig. His work includes the Machine Learning Control textbook, a 2021 flow-control study with few sensors, and the xMLC toolkit for learning control laws. Since 2025, he is a Distinguished Professor at Shenzhen University and an Adjunct Professor at Harbin Institute of Technology, Shenzhen; in 2026, he leads a machine-learning project on drone aerodynamics.
- Works
- 481
- Citations
- 16,048
- h-index
- 57
Publication numbers from OpenAlex, as of 18 October 2026.
Timeline
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1989–1992
Physics study in Göttingen
He earns a physics diploma from the University of Göttingen in 1989 and completes a physics doctorate there in 1992.
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2000–2020
Berlin professor and PPRIME director
From 2000 to 2009, he serves as professor and Chair of Reduced-Order Modeling for Flow Control at the Technical University of Berlin. In 2009, he joins PPRIME in Poitiers as a CNRS research director and heads the Senior ANR Chair of Excellence there from 2010 to 2015. His CNRS research director role at LIMSI and PPRIME runs through 2020.
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2012
An APS Fellowship
He becomes a Fellow of the American Physical Society in 2012.
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2015–2019
Flow control at Braunschweig
From 2015 to 2019, he serves as professor and Chair of Flow Modeling and Control at TU Braunschweig.
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2016
Machine learning control
In 2016, he coauthors Machine Learning Control – Taming Nonlinear Dynamics and Turbulence with Thomas Duriez and Steven L. Brunton. The book sets out machine-learning methods for controlling turbulence and other nonlinear systems.
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2021
Flow control with few sensors
At the 2021 APS Division of Fluid Dynamics meeting, he and colleagues show that machine-learning control stabilizes a simulated cylinder wake and reduces drag with few sensor signals.
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2022
The xMLC toolkit
In 2022, he coauthors xMLC, an open-source toolkit that uses genetic programming to learn open- and closed-loop control laws.
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Since 2025
Professor at Shenzhen University
Since 2025, he is a Distinguished Professor at Shenzhen University and an Adjunct Professor at Harbin Institute of Technology, Shenzhen. In 2026, he leads a project on drone aerodynamic optimization using multi-fidelity modeling and machine learning.
Career
- Since 2025Shenzhen UniversityDistinguished Professor
- Since 2025Harbin Institute of TechnologyAdjunct Professor
- 2025–2026Shenzhen Technology University
- 2024–2025Universidad Carlos III de Madrid
- Until 2020LIMSICNRS Research Director
- 2018–2023Université Paris-Saclay
- 2016–2021Université Paris-Sud
- 2015–2019Technical University of BraunschweigProfessor and Chair of Flow Modeling and Control
- 2014–2020Technische Universität Braunschweig
- 2014–2023Institut des Sciences de l'Information et de leurs Interactions
- 2010–2017École Nationale Supérieure de Mécanique et d'Aérotechnique
- 2010–2017Université de Poitiers
- 2009–2020PPRIMECNRS Research Director, Head of Senior ANR Chair of Excellence
- 2009–2018CNRS Ingénierie
- 2009–2022CNRS
- 2000–2009Technische Universität BerlinProfessor and Chair of Reduced-Order Modeling for Flow Control
- 1991–1995Max Planck Society
Education
- 1992University of GöttingenPhD
- 1989University of GöttingenPhysics diploma
Most-cited works
- Machine Learning for Fluid Mechanics
- A hierarchy of low-dimensional models for the transient and post-transient cylinder wake
- Closed-Loop Turbulence Control: Progress and Challenges
- Three-dimensional coherent structures in a swirling jet undergoing vortex breakdown: stability analysis and empirical mode construction
- On the transition of the cylinder wake
Colleagues
Sources and credits
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