• Login
    View Item 
    •   SMARTech Home
    • College of Engineering (CoE)
    • Daniel Guggenheim School of Aerospace Engineering (AE)
    • Aerospace Systems Design Laboratory (ASDL)
    • Aerospace Systems Design Laboratory Publications
    • View Item
    •   SMARTech Home
    • College of Engineering (CoE)
    • Daniel Guggenheim School of Aerospace Engineering (AE)
    • Aerospace Systems Design Laboratory (ASDL)
    • Aerospace Systems Design Laboratory Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Deep Gaussian Process Enabled Surrogate Models for Aerodynamic Flows

    Thumbnail
    View/Open
    DR_SciTech_2020__Deep_Gaussian_Process_Enabled_Surrogate_Models_for_Aerodynamic_Flows.pdf (4.485Mb)
    Date
    2020-01
    Author
    Rajaram, Dushhyanth
    Puranik, Tejas G.
    Renganathan, Sudharshan Ashwin
    Sung, WoongJe
    Pinon Fischer, Olivia J.
    Mavris, Dimitri N.
    Metadata
    Show full item record
    Abstract
    Deep Gaussian process (DGP) models are multi-layered hierarchical generalizations of the well-known Gaussian process (GP) models widely used to construct surrogate models of aerodynamic quantities of interest. Combining the desirable features of GP models and deep neural networks (DNN), DGP models are known to perform well when training data is scarce and the behavior of the system response is highly non-stationary. In this paper, the performance of DGP models is evaluated against GP models. Detailed comparisons are made and conclusions are drawn in terms of training time, data requirements, predictive error, and robustness to choice of training design of experiments, among other metrics. Additionally, sensitivity and scalability analyses are conducted for the GP models to evaluate their usability. Finally, an adaptive construction of both models is presented, where the models are built sequentially by selecting points that maximize posterior variance. Several experiments are conducted with canonical test functions at varying input dimensions and a viscous transonic airfoil test case at 42 input dimensions. The experiments suggest that DGP models outperform traditional GP models in terms of accuracy but incur higher computational costs for training.
    URI
    http://hdl.handle.net/1853/62530
    Collections
    • Aerospace Systems Design Laboratory Publications [310]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology