The Parameterized Background Data-Weak (PBDW) method is a non-intrusive, reduced basis, real-time and in-situ data assimilation framework for physical systems modeled by parametrized Partial Differential Equations for steady-state problems. The key idea of the formulation is to seek an approximation to the true field employing projection-by-data, with a first contribution from a deduced background estimate from reduced modeling and a second contribution from an update state informed by the experimental observations (correction of model bias). The present study aims at extending the PBDW formulation for time-dependent problems and proposes a sequential version to deal with sampled data. The work focuses on a time integration in the reduced order model, a data-driven empirical enrichment of the model and a state prediction for future time steps.
Published on 06/06/21
Accepted on 06/06/21
Submitted on 06/06/21
Volume MS01 - Advanced Methods for Data Assimilation/Inverse Problems (Connected with Adaptive Modeling), 2021
Licence: CC BY-NC-SA license
Are you one of the authors of this document?