论文标题
基于机器学习的贝叶斯优化解决方案，用于尘土飞扬的非线性响应
A machine learning based Bayesian optimization solution to nonlinear responses in dusty plasmas
论文作者
论文摘要
非线性频率响应分析是一种广泛使用的方法，用于在存在非线性的情况下确定系统动力学。在灰尘等离子体中，可以通过单个粒子非线性响应分析来表征等离子体谷物的相互作用（例如，晶粒可充电波动），而谷物谷物非线性相互作用可以通过多粒子非线性反应分析确定。在这里，提出了一种基于机器学习的方法，用于确定等离子体中灰尘颗粒的非线性响应分析中运动方程。以贝叶斯方式搜索参数空间，可以有效优化匹配模拟的非线性响应曲线所需的参数，以实验测量的非线性响应曲线。
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 nonlinear response analysis, while grain-grain nonlinear interactions can be determined by a multi-particle nonlinear response analysis. Here, a machine learning-based method to determine the equation of motion in the nonlinear 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 nonlinear response curves to experimentally measured nonlinear response curves.
