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• W1836047100 abstract "SUMMARY Reduced order modeling plays an indispensible role for most real‐world complex models. The objective of this manuscript is to hybridize local and global sensitivity analysis methods to enable the application of reduced order modeling to complex nonlinear models, often encountered in real system design and analysis calculations, for example, nuclear reactors. This is achieved by first employing local variational methods to identify important nonlinear features of the original model that are required to reach a user‐defined accuracy for the reduced model. This information is obtained by sampling local first‐order derivatives of a pseudoresponse utilizing a modified representation of an infinite series expansion around some reference point. The resulting derivative information is aggregated in a subspace of dimension much less than the dimension of the input parameter space. The accuracy of the reduced model can be mathematically quantified using a bounding norm. Next, global sensitivity methods are employed to exhaustively search the reduced subspace for sensitivity information. The theory and implementation details of the proposed method are exposed in this manuscript. Numerical tests based on prototype nonlinear functions and radiation transport models with many input parameters and many responses are conducted as proof of principle. Copyright © 2012 John Wiley & Sons, Ltd." @default.
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• W1836047100 date "2012-07-01" @default.
• W1836047100 modified "2023-10-10" @default.
• W1836047100 title "Hybrid reduced order modeling applied to nonlinear models" @default.
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• W1836047100 doi "https://doi.org/10.1002/nme.4298" @default.
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