Inference of m-NLP data using radial basis function regression with center-evolving algorithm

An efficient technique is presented to infer space plasma density and satellite potentials from Langmuir probe measurements, using multivariate radial basis function (RBF) regression. This inference technique goes beyond analytic approaches which have been developed over nearly a century, and which remain in use in most lab and space plasma experiments. The method is assessed by applying it to synthetic data sets constructed with three-dimensional particle in cell (PIC) simulations of fixed-bias needle Langmuir probes proposed by Jacobsen, to determine a plasma parameter, independently of the temperature. Our approach follows machine learning techniques, whereby models are constructed on training data sets consisting of the simulated collected currents as a function of voltage, corresponding to known physical parameters such as plasma density and temperature, and satellite potential. Compared to standard approaches used in RBF regression, our approach proves to be particularly efficient when working with large training sets, by implementing an evolutive selection of optimal centers.

本文提出了一种高效的技术，通过多变量径向基函数（RBF）回归从朗缪尔探针测量中推断空间等离子体密度和卫星电位。该推断技术超越了分析方法的范畴，而分析方法自近一个世纪以来已得到发展，并仍在大多数实验室和空间等离子体实验中应用。本研究通过将该方法应用于由Jacobsen提出的固定偏置针状朗缪尔探针的三维粒子在细胞（PIC）模拟构建的合成数据集，以确定等离子体参数（独立于温度）进行评估。本方法遵循机器学习技术，在训练数据集上构建模型，该数据集包含作为电压函数的模拟收集电流，对应已知的物理参数，如等离子体密度和温度，以及卫星电位。与标准RBF回归中使用的方法相比，本研究的方法在处理大型训练集时尤为高效，通过实施最优中心的演化选择。