Least squares approach for initial data recovery in dynamic data-driven applications simulations

C. Douglas, Y. Efendiev, R. Ewing, V. Ginting, R. Lazarov*, M. Cole, G. Jones

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, we consider the initial data recovery and the solution update based on the local measured data that are acquired during simulations. Each time new data is obtained, the initial condition, which is a representation of the solution at a previous time step, is updated. The update is performed using the least squares approach. The objective function is set up based on both a measurement error as well as a penalization term that depends on the prior knowledge about the solution at previous time steps (or initial data). Various numerical examples are considered, where the penalization term is varied during the simulations. Numerical examples demonstrate that the predictions are more accurate if the initial data are updated during the simulations. © Springer-Verlag 2011.
Original languageEnglish (US)
Pages (from-to)365-375
Number of pages11
JournalComputing and Visualization in Science
Issue number8
StatePublished - May 19 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: Research of the authors is partially supported by NSF grantITR-0540136 and by award KUS-C1-016-04, made by King AbdullahUniversity of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Least squares approach for initial data recovery in dynamic data-driven applications simulations'. Together they form a unique fingerprint.

Cite this