Ding, Zhiyue and Matthews, Lorin S and Hyde, Truell W (2021) A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas. Machine Learning: Science and Technology, 2 (3). 035017. ISSN 2632-2153
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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.
Item Type: | Article |
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Subjects: | West Bengal Archive > Multidisciplinary |
Depositing User: | Unnamed user with email support@westbengalarchive.com |
Date Deposited: | 13 Jul 2023 04:24 |
Last Modified: | 07 Jun 2024 10:31 |
URI: | http://article.stmacademicwriting.com/id/eprint/1217 |