rbMIT

The rbMIT © MIT Software package implements in Matlab® all the general RB algorithms. The rbMIT © MIT Software package is intended to serve both (as Matlab® source) ”Developers” — numerical analysts and computational tool-builders — who wish to further develop the methodology, and (as Matlab® ”executables”) ”Users” — computational engineers and educators — who wish to rapidly apply the methodology to new applications. (”End-Users” of Worked Problems will also make use of the package, but in ”blackbox” fashion.) Requirements are (i) some but not extensive knowledge of both FE methods and RB methods, (ii) Matlab® Version 6.5 or newer on some reasonably fast platform, (iii) the Matlab® symbolic, pde, and optimizaton toolkits, and (iv) agreement to rbMIT © MIT usage, distribution, and citation terms and conditions upon download.

This software is also referenced in ORMS.

Keywords for this software

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References in zbMATH (referenced in 121 articles )

Showing results 21 to 40 of 121.
Sorted by year (citations)
  1. Milk, René; Rave, Stephan; Schindler, Felix: PyMOR -- generic algorithms and interfaces for model order reduction (2016)
  2. Néron, David; Ben Dhia, Hachmi; Cottereau, Régis: A decoupled strategy to solve reduced-order multimodel problems in the PGD and Arlequin frameworks (2016)
  3. Ohlberger, Mario; Smetana, Kathrin: Approximation of skewed interfaces with tensor-based model reduction procedures: application to the reduced basis hierarchical model reduction approach (2016)
  4. Quarteroni, Alfio; Manzoni, Andrea; Negri, Federico: Reduced basis methods for partial differential equations. An introduction (2016)
  5. Sirković, Petar; Kressner, Daniel: Subspace acceleration for large-scale parameter-dependent Hermitian eigenproblems (2016)
  6. Abdulle, Assyr; Bai, Yun; Vilmart, Gilles: Reduced basis finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems (2015)
  7. Benner, Peter; Feng, Lihong; Li, Suzhou; Zhang, Yongjin: Reduced-order modeling and ROM-based optimization of batch chromatography (2015)
  8. Benner, Peter; Gugercin, Serkan; Willcox, Karen: A survey of projection-based model reduction methods for parametric dynamical systems (2015)
  9. Devaud, Denis; Rozza, Gianluigi: Reduced basis approximation for the structural-acoustic design based on energy finite element analysis (RB-EFEA) (2015)
  10. Drohmann, Martin; Carlberg, Kevin: The ROMES method for statistical modeling of reduced-order-model error (2015)
  11. Hesthaven, Jan S.; Zhang, Shun; Zhu, Xueyu: Reduced basis multiscale finite element methods for elliptic problems (2015)
  12. Kaulmann, S.; Flemisch, B.; Haasdonk, B.; Lie, K.-a.; Ohlberger, M.: The localized reduced basis multiscale method for two-phase flows in porous media (2015)
  13. Lass, Oliver; Volkwein, Stefan: Parameter identification for nonlinear elliptic-parabolic systems with application in lithium-ion battery modeling (2015)
  14. Martini, Immanuel; Rozza, Gianluigi; Haasdonk, Bernard: Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system (2015)
  15. Néron, David; Boucard, Pierre-Alain; Relun, Nicolas: Time-space PGD for the rapid solution of 3D nonlinear parametrized problems in the many-query context (2015)
  16. Paul-Dubois-Taine, A.; Amsallem, D.: An adaptive and efficient greedy procedure for the optimal training of parametric reduced-order models (2015)
  17. Schauer, Volker; Linder, Christian: The reduced basis method in all-electron calculations with finite elements (2015)
  18. Taddei, T.; Perotto, S.; Quarteroni, A.: Reduced basis techniques for nonlinear conservation laws (2015)
  19. Wieland, Bernhard: Implicit partitioning methods for unknown parameter sets. In the context of the reduced basis method (2015)
  20. Zhang, Yongjin; Feng, Lihong; Li, Suzhou; Benner, Peter: An efficient output error estimation for model order reduction of parametrized evolution equations (2015)