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Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds

Abstract : We demonstrate, on a scramjet combustion problem, a constrained probabilistic learning approach that augments physics-based datasets with realizations that adhere to underlying constraints and scatter. The constraints are captured and delineated through diffusion maps, while the scatter is captured and sampled through a projected stochastic differential equation. The objective function and constraints of the optimization problem are then efficiently framed as non-parametric conditional expectations. Different spatial resolutions of a large-eddy simulation filter are used to explore the robustness of the model to the training dataset and to gain insight into the significance of spatial resolution on optimal design.
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https://hal-upec-upem.archives-ouvertes.fr/hal-02341912
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Submitted on : Sunday, November 17, 2019 - 9:57:08 AM
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Roger Ghanem, Christian Soize, C. Safta, X. Huan, G. Lacaze, et al.. Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds. Journal of Computational Physics, Elsevier, 2019, 399, pp.108930. ⟨10.1016/j.jcp.2019.108930⟩. ⟨hal-02341912⟩

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