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 120 articles )

Showing results 1 to 20 of 120.
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  1. Fu, Hongfei; Wang, Hong; Wang, Zhu: POD/DEIM reduced-order modeling of time-fractional partial differential equations with applications in parameter identification (2018)
  2. González, D.; Aguado, Jose V.; Cueto, E.; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco: kPCA-based parametric solutions within the PGD framework (2018)
  3. Grenier, Emmanuel; Helbert, Celine; Louvet, Violaine; Samson, Adeline; Vigneaux, Paul: Population parametrization of costly black box models using iterations between SAEM algorithm and Kriging (2018)
  4. Lukassen, Axel Ariaan; Kiehl, Martin: Parameter estimation with model order reduction for elliptic differential equations (2018)
  5. Martini, Immanuel; Haasdonk, Bernard; Rozza, Gianluigi: Certified reduced basis approximation for the coupling of viscous and inviscid parametrized flow models (2018)
  6. Schmidt, Andreas; Haasdonk, Bernard: Reduced basis approximation of large scale parametric algebraic Riccati equations (2018)
  7. Chen, Peng; Quarteroni, Alfio; Rozza, Gianluigi: Reduced basis methods for uncertainty quantification (2017)
  8. Horger, Thomas; Wohlmuth, Barbara; Dickopf, Thomas: Simultaneous reduced basis approximation of parameterized elliptic eigenvalue problems (2017)
  9. Iapichino, L.; Ulbrich, S.; Volkwein, Stefan: Multiobjective PDE-constrained optimization using the reduced-basis method (2017)
  10. Jiang, Jiahua; Chen, Yanlai; Narayan, Akil: Offline-enhanced reduced basis method through adaptive construction of the surrogate training set (2017)
  11. Lass, Oliver; Ulbrich, Stefan: Model order reduction techniques with a posteriori error control for nonlinear robust optimization governed by partial differential equations (2017)
  12. Welper, G.: Interpolation of functions with parameter dependent jumps by transformed snapshots (2017)
  13. Antonietti, Paola F.; Pacciarini, Paolo; Quarteroni, Alfio: A discontinuous Galerkin reduced basis element method for elliptic problems (2016)
  14. 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)
  15. Courard, Amaury; Néron, David; Ladevèze, Pierre; Ballere, Ludovic: Integration of PGD-virtual charts into an engineering design process (2016)
  16. Hesthaven, Jan S.; Zhang, Shun: On the use of ANOVA expansions in reduced basis methods for parametric partial differential equations (2016)
  17. Iapichino, Laura; Trenz, Stefan; Volkwein, Stefan: Reduced-order multiobjective optimal control of semilinear parabolic problems (2016)
  18. Ladevèze, Pierre: On reduced models in nonlinear solid mechanics (2016)
  19. Liao, Qifeng; Lin, Guang: Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs (2016)
  20. Milk, René; Rave, Stephan; Schindler, Felix: PyMOR -- generic algorithms and interfaces for model order reduction (2016)

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