Stochastic estimation using modern methods in machine learning

Andrew S. Tenney, Mark N. Glauser, Zachary P. Berger

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    Abstract

    In many areas of data science, Deep Neural Networks have exhibited a remarkable ability to learn complex, non-linear relationships between sets of variables. In this paper, we apply this network architecture to the task of stochastic estimation, first proposed by Adrian over 40 years ago. Two Deep Neural Networks (DNNs) are trained to estimate the pressure at selected locations in azimuthal and stream-wise arrays of pressure transducers situated just outside a Mach 0.6 jet, given instantaneous pressure measurements made at other locations in the arrays. The estimated pressure is compared with the instantaneous pressure fluctuations measured at each of the selected locations, as well as the estimates made using traditional Linear Stochastic Estimation (LSE). The root-mean-square error between the values predicted by the DNN and those measured by the transducers is shown to be nearly identical to the error associated with the traditional LSE method in the case of the azimuthal array. For the stream-wise configuration, the DNN shows an approximately 60% reduction in error over the LSE model. In addition, some limitations and possible extensions of this method are discussed.

    Original languageEnglish (US)
    Title of host publicationAIAA Scitech 2019 Forum
    PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
    ISBN (Print)9781624105784
    DOIs
    StatePublished - Jan 1 2019
    EventAIAA Scitech Forum, 2019 - San Diego, United States
    Duration: Jan 7 2019Jan 11 2019

    Publication series

    NameAIAA Scitech 2019 Forum

    Conference

    ConferenceAIAA Scitech Forum, 2019
    Country/TerritoryUnited States
    CitySan Diego
    Period1/7/191/11/19

    All Science Journal Classification (ASJC) codes

    • Aerospace Engineering

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