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    A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas

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    Date
    2021-06
    Author
    Ding, Zhiyue
    Matthews, Lorin
    Hyde, Truell
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    Abstract
    Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma–grain interaction (e.g. grain charging fluctuations) can be characterized by a single-particle non-linear response analysis, while grain–grain non-linear interactions can be determined by a multi-particle non-linear response analysis. Here a machine learning-based method to determine the equation of motion in the non-linear response analysis for dust particles in plasmas is presented. Searching the parameter space in a Bayesian manner allows an efficient optimization of the parameters needed to match simulated non-linear response curves to experimentally measured non-linear response curves.
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    https://hdl.handle.net/2104/11776
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    Copyright © Baylor® University All rights reserved. Legal Disclosures.
    Baylor University Waco, Texas 76798 1-800-BAYLOR-U
    Baylor University Libraries | One Bear Place #97148 | Waco, TX 76798-7148 | 254.710.2112 | Contact: libraryquestions@baylor.edu
    If you find any errors in content, please contact librarywebmaster@baylor.edu
    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    TDL
    Theme by 
    Atmire NV