On the matching of eigensolutions to parametric partial differential equations
Type
Conference PaperKAUST Department
Applied Mathematics and Computational Sciences (AMCS) King Abdullah University of Science and Technology Thuwal, 23955-6900, Kingdom of Saudi ArabiaComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
KAUST Grant Number
CRG2020Date
2022-11-24Permanent link to this record
http://hdl.handle.net/10754/679902
Metadata
Show full item recordAbstract
In this paper a novel numerical approximation of parametric eigenvalue problems is presented. We motivate our study with the analysis of a POD reduced order model for a simple one dimensional example. In particular, we introduce a new algorithm capable to track the matching of eigenvalues when the parameters vary.Citation
Alghamdi, M., Bertrand, F., Boffi, D., Bonizzoni, F., Halim, A., & Priyadarshi, G. (2022). On the matching of eigensolutions to parametric partial differential equations. 8th European Congress on Computational Methods in Applied Sciences and Engineering. https://doi.org/10.23967/eccomas.2022.213Sponsors
The work of F. Bertrand, D. Boffi, and A. Halim was supported by the Competitive Research Grants Program CRG2020 “Synthetic data-driven model reduction methods for modal analysis” awarded by the King Abdullah University of Science and Technology (KAUST). D. Boffi is member of the INdAM Research group GNCS and his research is partially supported by IMATI/CNR and by PRIN/MIUR. F. Bonizzoni is member of the INdAM Research group GNCS and her work is part of a project that has received funding from the European Research Council ERC under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 865751).Publisher
CIMNEConference/Event name
ECCOMAS Congress 2022 - 8th European Congress on Computational Methods in Applied Sciences and EngineeringarXiv
2207.06145Additional Links
https://www.scipedia.com/public/Alghamdi_et_al_2022aae974a485f413a2113503eed53cd6c53
10.23967/eccomas.2022.213