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dc.contributor.authorDing, Zhiyue
dc.contributor.authorMatthews, Lorin
dc.contributor.authorHyde, Truell
dc.date.accessioned2022-03-21T19:33:50Z
dc.date.available2022-03-21T19:33:50Z
dc.date.issued2021-06
dc.identifier.citationMachine Learning Science and Technology, 2, 035017, 2021en_US
dc.identifier.urihttps://hdl.handle.net/2104/11776
dc.description.abstractNonlinear 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.en_US
dc.language.isoenen
dc.publisherIOP PUblishingen_US
dc.titleA machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmasen_US
dc.typeArticleen
dc.identifier.doi10.1088/2632-2153/abe7b7


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