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.
This software is also referenced in ORMS.
Keywords for this software
References in zbMATH (referenced in 139 articles )
Showing results 1 to 20 of 139.
- Alla, Alessandro; Haasdonk, Bernard; Schmidt, Andreas: Feedback control of parametrized PDEs via model order reduction and dynamic programming principle (2020)
- Degen, Denise; Veroy, Karen; Wellmann, Florian: Certified reduced basis method in geosciences. Addressing the challenge of high-dimensional problems (2020)
- Glau, Kathrin; Kressner, Daniel; Statti, Francesco: Low-rank tensor approximation for Chebyshev interpolation in parametric option pricing (2020)
- Larion, Ygee; Zlotnik, Sergio; Massart, Thierry J.; Díez, Pedro: Building a certified reduced basis for coupled thermo-hydro-mechanical systems with goal-oriented error estimation (2020)
- Pichi, Federico; Quaini, Annalisa; Rozza, Gianluigi: A reduced order modeling technique to study bifurcating phenomena: application to the Gross-Pitaevskii equation (2020)
- Schmidt, Andreas; Wittwar, Dominik; Haasdonk, Bernard: Rigorous and effective a-posteriori error bounds for nonlinear problems -- application to RB methods (2020)
- Sevilla, Ruben; Zlotnik, Sergio; Huerta, Antonio: Solution of geometrically parametrised problems within a CAD environment via model order reduction (2020)
- Zhang, Zhenying; Hesthaven, Jan S.: Rare event simulation for large-scale structures with local nonlinearities (2020)
- Chakir, R.; Maday, Y.; Parnaudeau, P.: A non-intrusive reduced basis approach for parametrized heat transfer problems (2019)
- Crisovan, R.; Torlo, D.; Abgrall, R.; Tokareva, S.: Model order reduction for parametrized nonlinear hyperbolic problems as an application to uncertainty quantification (2019)
- Guo, Mengwu; Hesthaven, Jan S.: Data-driven reduced order modeling for time-dependent problems (2019)
- Hain, Stefan; Ohlberger, Mario; Radic, Mladjan; Urban, Karsten: A hierarchical a posteriori error estimator for the reduced basis method (2019)
- 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)
- Pichi, Federico; Rozza, Gianluigi: Reduced basis approaches for parametrized bifurcation problems held by non-linear von Kármán equations (2019)
- Pla, Francisco; Herrero, Henar: Reduced basis method applied to eigenvalue problems from convection (2019)
- Zhang, Zhenying; Guo, Mengwu; Hesthaven, Jan S.: Model order reduction for large-scale structures with local nonlinearities (2019)
- Fu, Hongfei; Wang, Hong; Wang, Zhu: POD/DEIM reduced-order modeling of time-fractional partial differential equations with applications in parameter identification (2018)
- González, D.; Aguado, Jose V.; Cueto, E.; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco: kPCA-based parametric solutions within the PGD framework (2018)
- 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)
- Guo, Mengwu; Hesthaven, Jan S.: Reduced order modeling for nonlinear structural analysis using Gaussian process regression (2018)