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Conference Papers Year : 2015

Inverse Reduced-Order Modeling


We propose a general probabilistic formulation of reduced-order modeling in the case the system state is hidden and characterized by some uncertainty. The objective is to integrate noisy and incomplete observations in the process of building a reduced-order model. We call this problematic inverse reduced-order modeling. This problematic arises in many scientific domains where there exists a need of accurate low-order descriptions of highly-complex phenomena, which can not be directly and/or deterministically observed. Among others, it concerns geophysical studies dealing with image data, which are important for the characterization of global warming or the prediction of natural disasters.
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Dates and versions

hal-01245051 , version 1 (17-12-2015)


  • HAL Id : hal-01245051 , version 1


Patrick Héas, Cédric Herzet. Inverse Reduced-Order Modeling. Reduced Basis, POD and PGD Model Reduction Techniques, Nov 2015, Cachan, France. ⟨hal-01245051⟩
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