Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Bauerheim, Michael"'
Autor:
Catalani, Giovanni, Agarwal, Siddhant, Bertrand, Xavier, Tost, Frederic, Bauerheim, Michael, Morlier, Joseph
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for differe
Externí odkaz:
http://arxiv.org/abs/2407.19916
We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, e
Externí odkaz:
http://arxiv.org/abs/2305.02116
Publikováno v:
In Journal of Computational Physics 1 January 2025 520
Autor:
Cheng, Lionel, Illarramendi, Ekhi Ajuria, Bogopolsky, Guillaume, Bauerheim, Michael, Cuenot, Benedicte
The Poisson equation is critical to get a self-consistent solution in plasma fluid simulations used for Hall effect thrusters and streamer discharges, since the Poisson solution appears as a source term of the unsteady nonlinear flow equations. As a
Externí odkaz:
http://arxiv.org/abs/2109.13076
The resolution of the Poisson equation is usually one of the most computationally intensive steps for incompressible fluid solvers. Lately, Deep Learning, and especially Convolutional Neural Networks (CNN), has been introduced to solve this equation,
Externí odkaz:
http://arxiv.org/abs/2109.09363
Effects of boundary conditions in fully convolutional networks for learning spatio-temporal dynamics
Autor:
Alguacil, Antonio, Pinto, Wagner Gonçalves, Bauerheim, Michael, Jacob, Marc C., Moreau, Stéphane
Accurate modeling of boundary conditions is crucial in computational physics. The ever increasing use of neural networks as surrogates for physics-related problems calls for an improved understanding of boundary condition treatment, and its influence
Externí odkaz:
http://arxiv.org/abs/2106.11160
Reproducibility of a deep-learning fully convolutional neural network is evaluated by training several times the same network on identical conditions (database, hyperparameters, hardware) with non-deterministic Graphics Processings Unit (GPU) operati
Externí odkaz:
http://arxiv.org/abs/2105.05482
Publikováno v:
In Proceedings of the Combustion Institute 2024 40(1-4)
Autor:
Catalani, Giovanni, Costero, Daniel, Bauerheim, Michael, Zampieri, Luca, Chapin, Vincent, Gourdain, Nicolas, Baqué, Pierre
Publikováno v:
In Computers and Fluids 30 January 2023 251
Saturation of a turbulent mixing layer over a cavity: response to harmonic forcing around mean flows
Publikováno v:
Journal of Fluid Mechanics, 853 (2018)
Turbulent mixing layers over cavities can couple with acoustic waves and lead to undesired oscillations. To understand the nonlinear aspects of this phenomenon, a turbulent mixing layer over a deep cavity at Reynolds number 150 000 is considered and
Externí odkaz:
http://arxiv.org/abs/1711.00273