Abstract
Many applications from science and engineering are based on parametrized evolution equations and depend on time-consuming parameter studies or need to ensure critical constraints on the simulation time. For both settings, model order reduction by the reduced basis methods is a suitable means to reduce computational time. In this proceedings, we show the applicability of the reduced basis framework to a finite volume scheme of a parametrized and highly nonlinear convection-diffusion problem with discontinuous solutions. The complexity of the problem setting requires the use of several new techniques like parametrized empirical operator interpolation, efficient a posteriori error estimation and adaptive generation of reduced data. The latter is usually realized by an adaptive search for base functions in the parameter space. Common methods and effects are shortly revised in this presentation and supplemented by the analysis of a new strategy to adaptively search in the time domain for empirical interpolation data.
MSC2010: 65M08, 65J15, 65Y2
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References
Barrault, M., Maday, Y., Nguyen, N., Patera, A.: An ’empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations. C. R. Math. Acad. Sci. Paris Series I 339, 667–672 (2004)
Drohmann, M., Haasdonk, B., Ohlberger, M.: Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation. Tech. rep., FB10, University of MĂĽnster (2010)
Eftang, J.L., Patera, A.T., Rønquist, E.M.: An hp Certified Reduced Basis Method for Parametrized Parabolic Partial Differential Equations. Technical report, MIT, Cambridge, 2009. Submitted to SISC
Haasdonk, B. and Ohlberger, M.: Reduced basis method for finite volume approximations of parametrized evolution equations. M2AN Math. Model. Numer. Anal., 42(2):277-302 (2008)
Haasdonk, B., Dihlmann, M., Ohlberger, M.: A training set and multiple bases generation approach for parametrized model reduction based on adaptive grids in parameter space. Tech. rep., University of Stuttgart (submitted) (2010)
Haasdonk, B., Ohlberger, M.: Adaptive basis enrichment for the reduced basis method applied to finite volume schemes. In: Proc. 5th International Symposium on Finite Volumes for Complex Applications, pp. 471–478 (2008)
Patera, A., Rozza, G.: Reduced Basis Approximation and a Posteriori Error Estimation for Parametrized Partial Differential Equations. MIT (2007). http://augustine.mit.edu/methodology/methodology_bookPartI.htm. Version 1.0, Copyright MIT 2006-2007, to appear in (tentative rubric) MIT Pappalardo Graduate Monographs in Mechanical Engineering
Acknowledgements
This work was supported by the German Science Foundation (DFG) under the contract number OH 98/2-1. The second author was supported by the Baden WĂĽrttemberg Stiftung gGmbH.
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Drohmann, M., Haasdonk, B., Ohlberger, M. (2011). Adaptive Reduced Basis Methods for Nonlinear Convection–Diffusion Equations. In: Fořt, J., Fürst, J., Halama, J., Herbin, R., Hubert, F. (eds) Finite Volumes for Complex Applications VI Problems & Perspectives. Springer Proceedings in Mathematics, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20671-9_39
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DOI: https://doi.org/10.1007/978-3-642-20671-9_39
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