A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas. Issue 3 (14th June 2021)

• Record Type: Journal Article
• Authors: Ding, Zhiyue; Matthews, Lorin S; Hyde, Truell W
• Is Part Of: Machine learning: science and technology. Volume 2:Issue 3(2021)
• Publication Date: 2021-06-14
• Subjects: dusty plasma -- machine learning -- non-linear dynamics; 006.31
• DOI: 10.1088/2632-2153/abe7b7
• Languages: English
• ISSNs: 2632-2153

Abstract

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.