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Conference papers

Inverse Reduced-Order Modeling

Patrick Héas 1 Cédric Herzet 2 
1 ASPI - Applications of interacting particle systems to statistics
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique
2 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Inria Rennes – Bretagne Atlantique
Abstract : 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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Submitted on : Thursday, December 17, 2015 - 2:41:47 PM
Last modification on : Friday, May 20, 2022 - 9:04:52 AM


  • 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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