Abstract This chapter gives the derivatives of an equation-constrained functional in order to help the derivation of a Taylor-based surrogate model. Two approaches are described, the Tangent-on-Tangent approach and the Tangent-on-Reverse approach according to the two composite differentiation modes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Algorithmic Differentiation for Second Derivatives


    Beteiligte:


    Erscheinungsdatum :

    21.07.2018


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Derivatives for Eigenvalues and Eigenvectors via Analytic Reverse Algorithmic Differentiation

    He, Sicheng / Jonsson, Eirikur / Martins, Joaquim R. R. A. | AIAA | 2022


    Using algorithmic differentiation for uncertainty analysis

    Hieu, Mai Trung / Nowak, Wolfgang / Kopmann, Rebekka | HENRY – Bundesanstalt für Wasserbau (BAW) | 2015

    Freier Zugriff

    Efficient Algorithmic Differentiation Techniques for Turbo-machinery Design

    Sagebaum, Max / Özkaya, Emre / Gauger, Nicolas R. et al. | AIAA | 2017


    First-order uncertainty analysis using Algorithmic Differentiation of morphodynamic models

    Villaret, Catherine / Kopmann, Rebekka / Wyncoll, David et al. | HENRY – Bundesanstalt für Wasserbau (BAW) | 2016

    Freier Zugriff

    First-order uncertainty analysis using Algorithmic Differentiation of morphodynamic models

    Villaret, Catherine / Kopmann, Rebekka / Wyncoll, David et al. | BASE | 2016

    Freier Zugriff