Abstract
With target applications characterized by computationally intensive parametrized problems that require repeated evaluation, it is clear that we need to seek alternatives to simply solving the full problem many times. This is exactly where reduced models have its place and we are now ready to dive deeper into a discussion of central elements of the certified reduced basis method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
P. Binev, A. Cohen, W. Dahmen, R. DeVore, G. Petrova, P. Wojtaszczyk, Convergence rates for greedy algorithms in reduced basis methods. SIAM J. Math. Anal. 43, 1457–1472 (2011)
R. DeVore, G. Petrova, P. Wojtaszczyk, Greedy algorithms for reduced bases in Banach spaces. Constr. Approx. 37, 455–466 (2013)
A. Buffa, Y. Maday, A.T. Patera, C. Prud’homme, G. Turinici, A priori convergence of the greedy algorithm for the parametrized reduced basis method. ESAIM Math. Model. Numer. Anal. 46, 595–603 (2012)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 The Author(s)
About this chapter
Cite this chapter
Hesthaven, J.S., Rozza, G., Stamm, B. (2016). Reduced Basis Methods. In: Certified Reduced Basis Methods for Parametrized Partial Differential Equations. SpringerBriefs in Mathematics. Springer, Cham. https://doi.org/10.1007/978-3-319-22470-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-22470-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22469-5
Online ISBN: 978-3-319-22470-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)