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.


References in zbMATH (referenced in 96 articles )

Showing results 1 to 20 of 96.
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  1. Antonietti, Paola F.; Pacciarini, Paolo; Quarteroni, Alfio: A discontinuous Galerkin reduced basis element method for elliptic problems (2016)
  2. Cadou, J.M.; Boumediene, F.; Guevel, Y.; Girault, G.; Duigou, L.; Daya, E.M.; Potier-Ferry, M.: A high order reduction-correction method for Hopf bifurcation in fluids and for viscoelastic vibration (2016)
  3. Courard, Amaury; Néron, David; Ladevèze, Pierre; Ballere, Ludovic: Integration of PGD-virtual charts into an engineering design process (2016)
  4. Hesthaven, Jan S.; Zhang, Shun: On the use of ANOVA expansions in reduced basis methods for parametric partial differential equations (2016)
  5. Milk, René; Rave, Stephan; Schindler, Felix: PyMOR -- generic algorithms and interfaces for model order reduction (2016)
  6. Néron, David; Dhia, Hachmi Ben; Cottereau, Régis: A decoupled strategy to solve reduced-order multimodel problems in the PGD and Arlequin frameworks (2016)
  7. Quarteroni, Alfio; Manzoni, Andrea; Negri, Federico: Reduced basis methods for partial differential equations. An introduction (2016)
  8. Sirković, Petar; Kressner, Daniel: Subspace acceleration for large-scale parameter-dependent Hermitian eigenproblems (2016)
  9. Abdulle, Assyr; Bai, Yun; Vilmart, Gilles: Reduced basis finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems (2015)
  10. Benner, Peter; Feng, Lihong; Li, Suzhou; Zhang, Yongjin: Reduced-order modeling and ROM-based optimization of batch chromatography (2015)
  11. Devaud, Denis; Rozza, Gianluigi: Reduced basis approximation for the structural-acoustic design based on energy finite element analysis (RB-EFEA) (2015)
  12. Drohmann, Martin; Carlberg, Kevin: The ROMES method for statistical modeling of reduced-order-model error (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. Schauer, Volker; Linder, Christian: The reduced basis method in all-electron calculations with finite elements (2015)
  16. Taddei, T.; Perotto, S.; Quarteroni, A.: Reduced basis techniques for nonlinear conservation laws (2015)
  17. Wieland, Bernhard: Implicit partitioning methods for unknown parameter sets. In the context of the reduced basis method (2015)
  18. Zhang, Yongjin; Feng, Lihong; Li, Suzhou; Benner, Peter: An efficient output error estimation for model order reduction of parametrized evolution equations (2015)
  19. Antil, Harbir; Heinkenschloss, Matthias; Sorensen, Danny C.: Application of the discrete empirical interpolation method to reduced order modeling of nonlinear and parametric systems (2014)
  20. Bebendorf, Mario; Maday, Yvon; Stamm, Benjamin: Comparison of some reduced representation approximations (2014)

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