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- W2259465784 abstract "The objective of this paper is to investigate how noisy and incomplete observations can be integrated in the process of building a reduced-order model. This problematic arises in many scientific domains where there exists a need for accurate low-order descriptions of highly-complex phenomena, which can not be directly and/or deterministically observed. Within this context, the paper proposes a probabilistic framework for the construction of POD-Galerkin reduced-order models. Assuming a hidden Markov chain, the inference integrates the uncertainty of the hidden states relying on their posterior distribution. Simulations show the benefits obtained by exploiting the proposed framework." @default.
- W2259465784 created "2016-06-24" @default.
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- W2259465784 date "2015-10-08" @default.
- W2259465784 modified "2023-09-27" @default.
- W2259465784 title "Reduced-Order Modeling Of Hidden Dynamics" @default.
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