Documents dont l'auteur est "Houde, Sébastien"

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Nombre de documents: 7

Auteur ou autrice

Audefroy, C., Olivier, M., Houde, S., & Gosselin, F. (mai 2025). Evaluating flow-induced damping by solving a perturbation flutter problem [Résumé]. 32nd Annual Conference of the Computational Fluid Dynamics Society of Canada Canadian Society of Rheology Symposium (CSME-CFDSC-CSR 2025), Montréal, QC, Canada (1 page). Lien externe

Lamoureux, D., Audefroy, C., Labzour, O. T., Houde, S., & Gosselin, F. (2024). Evaluating flow-added damping on a hydrofoil by combining vibro-acoustics and doublet-lattice panel analyses. Flow, 4, 20 pages. Lien externe

Labzour, O. T., Karbasian, H. R., Houde, S., & Gosselin, F. (mai 2023). Combining reduced-order modeling and PINNs to model fluid-structure interactions [Résumé]. Canadian Society for Mechanical Engineering (CSME) / Computational Fluid Dynamics Society of Canada (CFD Canada). International congress, Sherbrooke, Qc, Canada (1 page). Publié dans Progress in Canadian Mechanical Engineering, 3. Lien externe

Lamoureux, D., Audefroy, C., Tazi-Labzour, O., Houde, S., & Gosselin, F. (mai 2023). Evaluating flow-added damping of hydrofoils by combining vibro-acoustics and doublet-lattice panel analyses [Résumé]. Canadian Society for Mechanical Engineering (CSME) / Computational Fluid Dynamics Society of Canada (CFD Canada). International congress, Sherbrooke, Qc, Canada (1 page). Publié dans Progress in Canadian Mechanical Engineering, 3. Lien externe

Raynaud, G., Houde, S., & Gosselin, F. (2022). ModalPINN: An extension of physics-informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors. Journal of Computational Physics, 464, 18 pages. Lien externe

Directeur ou directrice de recherche

Tazi Labzour, O. (2023). Combination of PINNs and Reduced-Order Modeling to Predict Vortex-Induced Vibrations [Mémoire de maîtrise, Polytechnique Montréal]. Disponible

Raynaud, G. (2021). Study of Physics-Informed Neural Networks to Solve Fluid-Structure Problems for Turbine-like Phenomena [Mémoire de maîtrise, Polytechnique Montréal]. Disponible

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