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.
Highlights
Machine learning has recently become one of the hottest analysis techniques in the scientific world as application of this powerful numerical method has proven useful in solving problems across a wide range of fields
A machine learning-based method is applied to nonlinear response problems in dusty plasmas
Figure. 2 shows the Bayesian-optimized simulated primary response curves of a single dust particle levitated in the plasma sheath in the Gaseous Electronics Conference (GEC) RF reference cell at a plasma power of 1.68 Watts and pressure of 40 mTorr
Summary
Machine learning (or deep learning) has recently become one of the hottest analysis techniques in the scientific world as application of this powerful numerical method has proven useful in solving problems across a wide range of fields. Understanding the physics behind dust particle behavior (i.e., investigating these factors) is one of the most important tasks in dusty plasmas. It is important to note that this framework is not limited to nonlinear response analysis, but can be applied to the more general case of physics problems where the experimental results can be reproduced by simulations. In these cases, undetermined physics quantities can be revealed efficiently (especially when the simulation is very computational expensive) by optimizing the simulations to experimental results in this Bayesian manner
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