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.

References in zbMATH (referenced in 121 articles )

Showing results 41 to 60 of 121.
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  1. Antil, Harbir; Heinkenschloss, Matthias; Sorensen, Danny C.: Application of the discrete empirical interpolation method to reduced order modeling of nonlinear and parametric systems (2014)
  2. Bebendorf, Mario; Maday, Yvon; Stamm, Benjamin: Comparison of some reduced representation approximations (2014)
  3. Benner, Peter; Sachs, Ekkehard; Volkwein, Stefan: Model order reduction for PDE constrained optimization (2014)
  4. Casenave, Fabien; Ern, Alexandre; Lelièvre, Tony: Accurate and online-efficient evaluation of the a posteriori error bound in the reduced basis method (2014)
  5. Chen, Peng; Quarteroni, Alfio; Rozza, Gianluigi: Comparison between reduced basis and stochastic collocation methods for elliptic problems (2014)
  6. Dahmen, Wolfgang; Plesken, Christian; Welper, Gerrit: Double greedy algorithms: reduced basis methods for transport dominated problems (2014)
  7. Hesthaven, Jan S.; Stamm, Benjamin; Zhang, Shun: Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods (2014)
  8. Ionita, A. C.; Antoulas, A. C.: Data-driven parametrized model reduction in the Loewner framework (2014)
  9. Ionita, Antonio C.; Antoulas, Athanasios C.: Case study: Parametrized reduction using reduced-basis and the Loewner framework (2014)
  10. Lass, Oliver; Volkwein, Stefan: Adaptive POD basis computation for parametrized nonlinear systems using optimal snapshot location (2014)
  11. Maier, I.; Haasdonk, B.: A Dirichlet-Neumann reduced basis method for homogeneous domain decomposition problems (2014)
  12. Ohlberger, Mario; Smetana, Kathrin: A dimensional reduction approach based on the application of reduced basis methods in the framework of hierarchical model reduction (2014)
  13. Pacciarini, Paolo; Rozza, Gianluigi: Stabilized reduced basis method for parametrized advection-diffusion PDEs (2014)
  14. Rozza, Gianluigi: Fundamentals of reduced basis method for problems governed by parametrized PDEs and applications (2014)
  15. Vitse, Matthieu; Néron, David; Boucard, Pierre-Alain: Virtual charts of solutions for parametrized nonlinear equations (2014)
  16. Abdulle, Assyr; Bai, Yun: Adaptive reduced basis finite element heterogeneous multiscale method (2013)
  17. Alla, Alessandro; Falcone, Maurizio: An adaptive POD approximation method for the control of advection-diffusion equations (2013)
  18. Antil, Harbir; Field, Scott E.; Herrmann, Frank; Nochetto, Ricardo H.; Tiglio, Manuel: Two-step greedy algorithm for reduced order quadratures (2013)
  19. Chen, Peng; Quarteroni, Alfio: Accurate and efficient evaluation of failure probability for partial different equations with random input data (2013)
  20. Chen, Peng; Quarteroni, Alfio; Rozza, Gianluigi: A weighted reduced basis method for elliptic partial differential equations with random input data (2013)