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• W4300865679 abstract "This study presents the development of a methodology for the construction of data-driven, parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with nonlinear and discontinuous features. Realistic computational budgets often constrain the size of the high-fidelity data set required to build a model with the desired predictive accuracy. In such cases, multifidelity models can be advantageous, as they leverage an abundance of inexpensive low-fidelity data in conjunction with high-fidelity training data to improve the model’s predictive accuracy. This study formulates a multifidelity reduced-order modeling method that uses nonlinear dimension reduction and Procrustes manifold alignment to project and transform data from disparate sources such that they lie in a common latent space. An initial feasibility assessment of the method is performed for emulating the flow over a two-dimensional transonic airfoil and a high-speed blunt body. It is observed that, for problems with high input space dimension and complex features, the predictive accuracy of the multifidelity nonlinear reduced-order models improves substantially over their linear counterparts. However, multifidelity linear models were superior to equivalent nonlinear models for smaller input space dimensions, which may provide a useful intuition for practitioners when constructing reduced-order models for their respective problems." @default.
• W4300865679 created "2022-10-04" @default.
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• W4300865679 date "2023-01-01" @default.
• W4300865679 modified "2023-09-27" @default.
• W4300865679 title "Manifold Alignment-Based Nonintrusive and Nonlinear Multifidelity Reduced-Order Modeling" @default.
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• W4300865679 doi "https://doi.org/10.2514/1.j061720" @default.
• W4300865679 hasPublicationYear "2023" @default.
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