default author photo

Michael Bauerheim

ISAE-SUPAERO Higher Institute of Aerospace Engineering

SCHOLARLY PAPERS

3

DOWNLOADS

174

TOTAL CITATIONS

2

Scholarly Papers (3)

Actions:
Email selected abstracts
1.

Empirical Uncertainty Quantification in Deep Learning: Applications to Fluid Dynamics

Number of pages: 38 Posted: 14 Jun 2025
Enrico Foglia, Benjamin Bobbia, Michael Bauerheim, Thierry Jardin, Stéphane Moreau and Pascal Fua
ISAE-SUPAERO Higher Institute of Aerospace Engineering, ISAE-SUPAERO Higher Institute of Aerospace Engineering, ISAE-SUPAERO Higher Institute of Aerospace Engineering, ISAE-SUPAERO Higher Institute of Aerospace Engineering, Université de Sherbrooke and affiliation not provided to SSRN
Downloads 87 (816,709)

Abstract:

Loading...

bayesian deep learning, frequentist uncertainty quantification, epistemic uncertainty, aleatoric uncertainty, trailing edge noise, airfoil aerodynamics

2.

Robustness and Reliability of State-Space, Frame-Based Modelling for Thermoacoustics

Number of pages: 23 Posted: 04 Apr 2024
Mathieu Cances, Laurent Gicquel, Franck Nicoud, Michael Bauerheim and Luc Giraud
affiliation not provided to SSRN, affiliation not provided to SSRN, Université de Montpellier, ISAE-SUPAERO Higher Institute of Aerospace Engineering and affiliation not provided to SSRN
Downloads 69 (883,195)

Abstract:

Loading...

Thermoacoustic instabilities, Low-order model, Modal expansion, State-space, Singular Value Decomposition

3.

Towards Scalable Surrogate Models Based on Neural Fields for Large Scale Aerodynamic Simulations

Number of pages: 37 Posted: 28 May 2025
Giovanni Catalani, Jean Fesquet, Michael Bauerheim, Frédéric Tost, Xavier Bertrand and Joseph Morlier
Airbus Operations SAS, affiliation not provided to SSRN, ISAE-SUPAERO Higher Institute of Aerospace Engineering, Airbus Operations SAS, Airbus Operations SAS and Université de Toulouse
Downloads 18 (1,470,786)
Citation 2

Abstract:

Loading...

Surrogate Modeling \sep Aerodynamics \sep Computational Fluid Dynamics \sep Deep Learning \sep Neural Fields