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Title: A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas
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.
Authors:
; ;
Award ID(s):
1740203
Publication Date:
NSF-PAR ID:
10326424
Journal Name:
Machine Learning: Science and Technology
Volume:
2
Issue:
3
Page Range or eLocation-ID:
035017
ISSN:
2632-2153
Sponsoring Org:
National Science Foundation
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