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Michaël Bauerheim

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Current position : Associate Professor at ISAE-Supaero (France) and Sherbrooke University (Canada)
E-mail : michael.bauerheim isae-supaero.fr
Phone : +33 5 61 33 80 85
Location : Building 38, office 223

Previous positions :
2015-2016 - Post-doctoral position at LMFA (France) and ETHZ (Switzerland) :

  • Aeroacoustic broad-band and tonal noise or rotor-stator interactions. LES and LNSE on the non-linear saturation of aeroacoustic sources.

2014-2015 - Post-doctoral position at IMFT (France) :

  • DNS and experiment of flame stabilization on a rotating cylinder.

2014 - Visiting scholar at Stanford University (USA) :

  • Uncertainty Quantification applied to symmetry breaking in thermoacoustics.

2012-2014 - PhD in thermo-acoustics at CERFACS and SNECMA (France) :

  • Theory and LES of thermoacoustic instabilities in annular combustion chambers.

2011 - Visiting scholar at Georgia Tech (USA) :

  • LES for combustion instabilities in methane-oxygen rocket engines.

Awards :
2014 - 3AF Clean Sky conference, nomination among the 10 best papers

2014 - 35th Int. Sytmposium on Combustion travelling grant 2014, and GFC travelling award with 2 papers accepted

2015 - Léopold Escande award for the best INP thesis 2014, delivered by the University of Toulouse

2015 - Award of Excellence for the contribution to "Ignition capability studies" in the FP7 European project LEMCOTECT

2015 - Paul Caseau award for the best PhD thesis on "Simulation and modelling", delivered by EDF

2015 - Paul Laffitte award for the best PhD thesis on "Combustion", delivered by the French Combustion Institute


  • Antonio Alguacil  : PhD student working on the propagation of acoustic waves in complex media using deep convolutional networks, in collaboration with Sherbrooke University
  • Ekhi Ajuria-Illaramendi  : PhD student working on the acceleration of the resolution of the Poisson equation by Deep Learning : application to incompressible and plasma solvers
  • Brice Martin  : PhD student working on Reinforcement Learning (RL) for opitmal aerodynamics and trajectories
  • Felix Zapata  : PhD student working on AI-assisted surrogate modelling and optimization of rocket combustion chambers
  • Andrea Arroyo  : PhD student working on aeroacoustics (self-noise) at high speed using LES and DNS, in collaboration with Sherbrooke University
  • Zhen Wei  : PhD student working on physics-informed deep learning using GCNN, in collaboration with EPFL and Neural Concept
  • Sandrine Berger (ETAP Project) : Postdoc focusing on Reinforcement Learning (RL) for optimal aerodynamics and controlling buffeting
  • Wagner Pinto (POLA3 project)  : Postdoc working on acoustic source detection and propagation using Deep Learning techniques
  • Charlélie Laurent (POLA3 project)  : Postdoc working sparse system identification, and application to inverse problems


High Performance Learning for Aeronautical Flow Physics
Developing innovative Artificial Intelligence techniques to tackle fluid mechanics and aerodynamics problems


I currently lead research at DAEP to develop AI-based CFD solvers. Some part of the CFD solvers are replaced by a deep neural network. It contribute to the effort of improving the performance of CFD codes, especially on GPUs. This activity is realized in collaboration with AI experts (Jolibrain) and Cerfacs (Helios group : http://cerfacs.fr/helios/)

Current PhDs :
- Antonio Alguacil : Developing new solvers for acoustic propagation.
- Ekhi Ajuria : Developing AI-based incompressible solvers.


Part of the AI activity is dedicated to flow reconstruction. In particular, denoising problems and missing data reconstruction are under investigation. First results on denoising and pressure reconstruction from PIV measurements show promising possibilities for deep learning to complement CFD and experiment in fluid mechanics. Part of the POLA3 project will investigate AI-based identification methods for non-linear systems

Current Postdocs :
- Charlélie Laurent
- Wagner Pinto


Prospective works are carried out to apply AI techniques to develop new optimization procedures. This current work is sustained by 2 research master projects and a postdoc fellow funded by DGA (ETAP projet), in particular using Reinforcement Learning. Part of the DGA project POLA3 will also investigate optimization problems.

Current Postdocs and PhDs :
- Wagner Pinto : optimization of bio-inspired wings (dragonfly)
- Sandrine Berger : Reinforcement Learning (RL) towards optimal flight and control (buffet)
- Brice Martin : Optimal unsteady aerodynamics by Artificial Intelligence
- Felix Jose Zapata-Usandivaras : AI-based optimization of a rocket engine

Using high-fidelity simulations to investigate aeroacoustics sources and propagation in complex flows

My research focuses on using high-fidelity simulations (Navier-Stokes CharlesX and LBM Palabos) to address aeroacoustics problems. Current investigations include :

  • Characterizing high-fidelity codes with acoustic benchmarks and applications. Typical cases ongoing are a drone’s rotor as well as trailing edge noise of high-Re airfoils.
  • Investigation of a jet-pump for propagation of acoustic waves in complex media, in collaboration with Stéphane Moreau from Sherbrooke Univ.
  • Hydrodynamic-Acoustic coupling using forced LES simulations, with applications to cavity flows and vortex-sound.

Current PhD :
- Andrea Giordano : High-fidelity simulations, theory and machine learning for high speed trailing edge noise.

Bio-inspired aerodynamics
Investigating the peculiar non-linear unsteady flows arising in bio-inspired geometries

My research focuses on using high-fidelity simulations (Navier-Stokes CharlesX and LBM Palabos) to investigate bio-inspired flows. Three typical cases are studied currently :

  • A boxfish, typical case of of high-efficient bluff body. This is investigated using LBM and experiment in collaboration with V. Chapin and E. Gowree
  • A dragonfly wing with both corrugations and rear arc, showing overall good aerodynamic performance at low Reynolds number. Interesting complex flows emerged in this case, such as rapid transition to chaos.
  • Flapping wing (insects, birds). Kinematics of flapping wings can be complex, especially since the associated flow is highly non-linear and unsteady. Finding methods to optimize these kinematics to extract maximum power in a highly efficient manner is challenging.

Current Postdocs and PhDs :
- Wagner Pinto : optimization of bio-inspired wings (dragonfly)
- Brice Martin : Optimal unsteady aerodynamics by Artificial Intelligence


Two PhDs positions are now available at ISAE-SUPAERO on applying deep learning to accelerate CFD solver. These positions are part of the ANR project FLOCCON (2021-2024), and follow the current work of E. Ajuria.

Internships at a master level are also possible. Feel free to send your CV and cover letter by mail.


A demonstration by Neural Concept (https://neuralconcept.com/) of our common research on deep learning for fast predictions of compressible flows is now available here

The ANR project FLOCCON, investigating a possible futur path of CFD through "Accelerating flow computation using convolutional networks", has been obtained. 2 PhDs positions are now available.

Invited talk at the ITN Magister Workshop, 15th September 2020.

Invited talk at the PASC19 Conference in Zurich on Machine Learning and High Performance Computing.



  • Sureli, a group at ISAE-Supaero focusing on fundamentals and applications of Reinforcement Learning methods (https://sureli.github.io/).

  • Jolibrain, a startup dedicated to Artificial Intelligence located in Toulouse (France)
  • Neural Concept (Switzerland), a startup dedicated to AI-assisted optimization, in particular in the field of aerodynamics (https://neuralconcept.com/)
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