Section: New Results

Reduced–order modeling of hidden dynamics

Participant : Patrick Héas.

This is a collaboration with Cédric Herzet (EPI FLUMINANCE, Inria Rennes–Bretagne Atlantique).

The objective of the paper [28] 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.