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• W3200923562 abstract "This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-order modelling method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e. with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce responses with inconsistent representations, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity reduced-order model without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity reduced-order model achieves comparable predictive accuracy at a lower computational cost. Furthermore, it is demonstrated that the proposed method can combine disparate fields without any adverse effect on predictive performance." @default.
• W3200923562 created "2021-09-27" @default.
• W3200923562 creator A5013837896 @default.
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• W3200923562 date "2021-09-01" @default.
• W3200923562 modified "2023-10-16" @default.
• W3200923562 title "Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment" @default.
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• W3200923562 doi "https://doi.org/10.1098/rspa.2021.0495" @default.
• W3200923562 hasPublicationYear "2021" @default.
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