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 136 articles )

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  1. Alla, Alessandro; Haasdonk, Bernard; Schmidt, Andreas: Feedback control of parametrized PDEs via model order reduction and dynamic programming principle (2020)
  2. Degen, Denise; Veroy, Karen; Wellmann, Florian: Certified reduced basis method in geosciences. Addressing the challenge of high-dimensional problems (2020)
  3. Schmidt, Andreas; Wittwar, Dominik; Haasdonk, Bernard: Rigorous and effective a-posteriori error bounds for nonlinear problems -- application to RB methods (2020)
  4. Sevilla, Ruben; Zlotnik, Sergio; Huerta, Antonio: Solution of geometrically parametrised problems within a CAD environment via model order reduction (2020)
  5. Zhang, Zhenying; Hesthaven, Jan S.: Rare event simulation for large-scale structures with local nonlinearities (2020)
  6. Chakir, R.; Maday, Y.; Parnaudeau, P.: A non-intrusive reduced basis approach for parametrized heat transfer problems (2019)
  7. Crisovan, R.; Torlo, D.; Abgrall, R.; Tokareva, S.: Model order reduction for parametrized nonlinear hyperbolic problems as an application to uncertainty quantification (2019)
  8. Guo, Mengwu; Hesthaven, Jan S.: Data-driven reduced order modeling for time-dependent problems (2019)
  9. Hain, Stefan; Ohlberger, Mario; Radic, Mladjan; Urban, Karsten: A hierarchical a posteriori error estimator for the reduced basis method (2019)
  10. Ibañez, R.; Abisset-Chavanne, E.; Cueto, E.; Ammar, A.; Duval, J. -L.; Chinesta, F.: Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction (2019)
  11. Pichi, Federico; Rozza, Gianluigi: Reduced basis approaches for parametrized bifurcation problems held by non-linear von Kármán equations (2019)
  12. Pla, Francisco; Herrero, Henar: Reduced basis method applied to eigenvalue problems from convection (2019)
  13. Zhang, Zhenying; Guo, Mengwu; Hesthaven, Jan S.: Model order reduction for large-scale structures with local nonlinearities (2019)
  14. Fu, Hongfei; Wang, Hong; Wang, Zhu: POD/DEIM reduced-order modeling of time-fractional partial differential equations with applications in parameter identification (2018)
  15. González, D.; Aguado, Jose V.; Cueto, E.; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco: kPCA-based parametric solutions within the PGD framework (2018)
  16. 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)
  17. Guo, Mengwu; Hesthaven, Jan S.: Reduced order modeling for nonlinear structural analysis using Gaussian process regression (2018)
  18. Lukassen, Axel Ariaan; Kiehl, Martin: Parameter estimation with model order reduction for elliptic differential equations (2018)
  19. Martini, Immanuel; Haasdonk, Bernard; Rozza, Gianluigi: Certified reduced basis approximation for the coupling of viscous and inviscid parametrized flow models (2018)
  20. Schmidt, Andreas; Haasdonk, Bernard: Reduced basis approximation of large scale parametric algebraic Riccati equations (2018)

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