Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Physics-Aware Surrogate-based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring


    Beteiligte:
    Ghassemi, Payam (Autor:in) / Behjat, Amir (Autor:in) / Zeng, Chen (Autor:in) / Lulekar, Sumeet S. (Autor:in) / Rai, Rahul (Autor:in) / Chowdhury, Souma (Autor:in)

    Kongress:

    AIAA AVIATION 2020 FORUM



    Erscheinungsdatum :

    01.01.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring

    Ghassemi, Payam / Lulekar, Sumeet S. / Chowdhury, Souma | AIAA | 2019


    Gaussian Surrogate Dimension Reduction for Efficient Reliability-Based Design Optimization

    Clark, Daniel L. / Bae, Harok / Forster, Edwin E. | AIAA | 2020


    Gaussian Surrogate Dimension Reduction for Efficient Reliability-Based Design Optimization

    Clark, Daniel L. / Bae, Harok / Forster, Edwin E. | AIAA | 2020


    DEVELOPMENT OF MDO FORMULATIONS BASED ON DISCIPLINARY SURROGATE MODELS BY GAUSSIAN PROCESSES

    Dubreuil, S. / Bartoli, N. / Berthelin, G. et al. | TIBKAT | 2021

    Freier Zugriff