Abstract In this chapter, we describe the Continuation Multi-Level Monte Carlo (C-MLMC) algorithm proposed in Collier et al. [1] and apply it to efficiently propagate operating and geometric uncertainties in internal and external aerodynamic simulations. The key idea of MLMC, presented in the previous chapter, is that one can draw MC samples simultaneously and independently on several approximations of the problem under investigation on a hierarchy of nested computational grids (levels). In the continuation algorithm (C-MLMC) the parameters that prescribe the number of levels and simulations per level are computed on the fly to further reduce the overall computational cost.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Continuation Multi-level Monte Carlo


    Beteiligte:


    Erscheinungsdatum :

    2018-07-21


    Format / Umfang :

    21 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Multi-level Monte Carlo Method

    Pons-Prats, Jordi / Bugeda, G. | Springer Verlag | 2018


    General Introduction to Monte Carlo and Multi-level Monte Carlo Methods

    Schmidt, Robin / Voigt, Matthias / Pisaroni, Michele et al. | Springer Verlag | 2018


    Continuation Multilevel Monte Carlo Evolutionary Algorithm for Robust Aerodynamic Shape Design

    Pisaroni, Michele / Nobile, Fabio / Leyland, Penelope | AIAA | 2018


    MULTI-MODEL MONTE CARLO ESTIMATORS FOR TRAJECTORY SIMULATION

    Warner, James / Niemoeller, Samantha C. / Morrill, Luke et al. | TIBKAT | 2021


    Multi-Model Monte Carlo Estimators for Trajectory Simulation

    Warner, James / Niemoeller, Samantha C. / Morrill, Luke et al. | AIAA | 2021