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

Liu, Guangdong; Marchand, R.

doi:10.1016/j.cpc.2022.108497

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…An alternative strategy is to successively train models with randomly selected small subsets of the entire training data set using the straightforward approach, while calculating the cost function on the full training set, and then carrying the optimal centers from one iteration to the next. This “center‐evolving strategy” is very efficient in finding near‐optimal centers for large training data sets and has proven to be as accurate as the straightforward extensive approach (Liu & Marchand, 2022 ). The RBF models here follow this procedure.…”

Section: Methodsmentioning

confidence: 99%

m‐NLP Inference Models Using Simulation and Regression Techniques

et al. 2023

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Section: Methodsmentioning

confidence: 99%

m‐NLP Inference Models Using Simulation and Regression Techniques

et al. 2023

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“…This is a standard application of what is known in machine learning as supervised training; that is, training with data sets containing independent variables, with corresponding labels; that is, dependent variables to be inferred. Several techniques have been developed in the field of machine learning to perform supervised training, including the deep learning neural network, radial basis functions (RBF) and kriging, (Powell 1992; Wackernagel 2003; Liu & Marchand 2022; Roberts, Yaida & Hanin 2022). In the following, we consider RBF inference, arguably one of the simplest regression approaches, because it is found to perform well with the problems considered.…”

Section: Methodsmentioning

confidence: 99%

Beyond analytic approximations with machine learning inference of plasma parameters and confidence intervals

Marchand

Shahsavani²,

Sánchez‐Arriaga³

2023

J. Plasma Phys.

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Machine learning techniques are used to construct models capable of inferring plasma state variables from non-emissive (LP) and emissive (EP) cylindrical Langmuir probes under conditions in which standard analytic theories are not applicable. Synthetic data sets, consisting of plasma parameters and probe characteristics computed kinetically in the orbital motion theory framework, are used to train and test regression models to infer electron densities, temperatures, and plasma potentials. Model skill metrics are introduced to determine uncertainty margins on inferred parameters, when models are applied to test sets not involved in the model optimization process. The different scalings and transformations required to obtain optimal accuracy are described in each case considered for both LPs and EPs. Excellent inferences are made for all three parameters considered from LP characteristics, but owing to the strong dependence on the plasma potential, and weak dependences on electron temperature and density with EPs, only plasma potential inferences are reported with acceptable accuracy for this type of probe. Our findings demonstrate that the combination of kinetic simulations and machine learning techniques is a promising and practical way to infer plasma parameters efficiently from cylindrical probes, under conditions beyond, and more general than those under which commonly used analytic approximations are valid.

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“…An alternative strategy is to successively train models with randomly selected small subsets of the entire training data set using the straightforward approach, while calculating the cost function on the full training set, and then carrying the optimal centers from one iteration to the next. This "center-evolving strategy" is very efficient in finding near-optimal centers for large training data sets and has proven to be as accurate as the straightforward extensive approach (Liu & Marchand, 2022). The RBF models here follow this procedure.…”

Section: Radial Basis Functionmentioning

confidence: 99%