Abstract
In this contribution, we aim to satisfy the demand for a publicly available benchmark for parametric model order reduction that is scalable both in degrees of freedom as well as parameter dimension.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alnæs, M.S.: UFL: a finite element form language, chap. 17. Springer (2012). https://doi.org/10.1146/10.1145/2566630
Alnæs, M.S., Blechta, J., Hake, J., Johansson, A., Kehlet, B., Logg, A., Richardson, C., Ring, J., Rognes, M.E., Wells, G.N.: The FEniCS project version 1.5. Arch. Numer. Softw. 3(100), 9–23 (2015). https://doi.org/10.1146/10.11588/ans.2015.100.20553
Balicki, L., Mlinarić, P., Rave, S., Saak, J.: System-theoretic model order reduction with pyMOR. Proc. Appl. Math. Mech. 19(1) (2019). https://doi.org/10.1146/10.1002/pamm.201900459
Ballani, J., Kressner, D.: Reduced basis methods: from low-rank matrices to low-rank tensors. SIAM J. Sci. Comput. 38(4), A2045–A2067 (2016). https://doi.org/10.1146/10.1137/15M1042784
Ballarin, F., Rozza, G.: RBniCS. https://mathlab.sissa.it/rbnics
Baur, U., Benner, P., Haasdonk, B., Himpe, C., Martini, I., Ohlberger, M.: Comparison of methods for parametric model order reduction of time-dependent problems. In: Benner, P., Cohen, A., Ohlberger, M., Willcox, K. (eds.) Model Reduction and Approximation: Theory and Algorithms, pp. 377–407. SIAM (2017). https://doi.org/10.1146/10.1137/1.9781611974829.ch9
Benner, P., Werner, S.W.R.: MORLAB – Model Order Reduction LABoratory (version 5.0) (2019). https://doi.org/10.1146/10.5281/zenodo.3332716. See also: http://www.mpi-magdeburg.mpg.de/projects/morlab
Chair of Automatic Control TUM, Technical University of Munich: psssMOR. https://www.mw.tum.de/rt/forschung/modellordnungsreduktion/software/psssmor/
Dolean, V., Jolivet, P., Nataf, F.: An Introduction to Domain Decomposition Methods: Algorithms, Theory, and Parallel Implementation. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (2015). https://doi.org/10.1146/10.1137/1.9781611974065.ch1
Geuzaine, C., Remacle, J.F.: Gmsh Reference Manual (2010). http://www.geuz.org/gmsh/doc/texinfo/gmsh.pdf
Haasdonk, B.: Reduced basis methods for parametrized PDEs–a tutorial introduction for stationary and instationary problems, chap. 2, pp. 65–136. SIAM Publications (2017). https://doi.org/10.1146/10.1137/1.9781611974829.ch2
Hesthaven, J.S., Rozza, G., Stamm, B.: Certified Reduced Basis Methods for Parametrized Partial Differential Equations, 1 edn. Springer Briefs in Mathematics. Springer International Publishing (2016). https://doi.org/10.1146/10.1007/978-3-319-22470-1. http://www.springer.com/us/book/9783319224695
Himpe, C.: emgr – EMpirical GRamian framework (version 5.4). http://gramian.de (2018). https://doi.org/10.1146/10.5281/zenodo.1241532
Logg, A., Mardal, K.A., Wells, G. (eds.): Automated Solution of Differential Equations by the Finite Element Method. Lecture Notes in Computational Science and Engineering, vol. 84, 1 edn. Springer, Berlin (2012)
Milk, R., Rave, S., Schindler, F.: pyMOR - generic algorithms and interfaces for model order reduction. SIAM J. Sci. Comput. 38(5), S194–S216 (2016). https://doi.org/10.1146/10.1137/15M1026614
Negri, F.: redbKIT Version 2.2. http://redbkit.github.io/redbKIT/ (2016)
Patera, A., Rozza, G.: Reduced basis approximation and a posteriori error estimation for parametrized partial differential equations. Version 1.0, Copyright MIT (2006). https://orms.mfo.de/project?id=316
pyMOR developers and contributors: pyMOR - model order reduction with Python. https://pymor.org
Quarteroni, A., Manzoni, A., Negri, F.: Reduced Basis Methods for Partial Differential Equations. La Matematica per il 3+2, vol. 92. Springer International Publishing (2016). https://doi.org/10.1146/10.1007/978-3-319-15431-2. https://www.springer.com/us/book/9783319154305
Rathgeber, F., Ham, D.A., Mitchell, L., Lange, M., Luporini, F., McRae, A.T.T., Bercea, G.T., Markall, G.R., Kelly, P.H.J.: Firedrake: automating the finite element method by composing abstractions. ACM Trans. Math. Softw. 43(3), 24:1–24:27 (2016). https://doi.org/10.1146/10.1145/2998441
Rozza, G., Huynh, D.B.P., Patera, A.T.: Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Arch. Comput. Methods Eng. 15(3), 229–275 (2008). https://doi.org/10.1146/10.1007/s11831-008-9019-9
Saak, J., Köhler, M., Benner, P.: M-M.E.S.S. – the matrix equations sparse solvers library. https://doi.org/10.1146/10.5281/zenodo.632897. See also: https://www.mpi-magdeburg.mpg.de/projects/mess
The MORwiki Community: MORwiki - Model Order Reduction Wiki. http://modelreduction.org
Acknowledgements
The authors would like to thank Christian Himpe, Petar Mlinarić and Steffen W. R. Werner for helpful comments and discussions during the creation of the model.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2044-390685587, Mathematics Münster: Dynamics-Geometry-Structure. Funded by German Bundesministerium für Bildung und Forschung (BMBF, Federal Ministry of Education and Research) under grant number 05M18PMA in the programme “Mathematik für Innovationen in Industrie und Dienstleistungen”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rave, S., Saak, J. (2021). A Non-stationary Thermal-Block Benchmark Model for Parametric Model Order Reduction. In: Benner, P., Breiten, T., Faßbender, H., Hinze, M., Stykel, T., Zimmermann, R. (eds) Model Reduction of Complex Dynamical Systems. International Series of Numerical Mathematics, vol 171. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-72983-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-72983-7_16
Published:
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-030-72982-0
Online ISBN: 978-3-030-72983-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)