Machine Learning: A novel approach to infer plasma parameters from probe measurements
Creators: Olowookere, Akinola
Publisher: name: University of Alberta Library
Year: 2022

<p>The research reported in this thesis is multidisciplinary in nature. It presents the use<br>
of kinetic simulations, state of the art data analysis, and machine learning techniques<br>
to infer plasma parameters and satellite parameter from Langmuir probe measurement.<br>
Physical parameters such as plasma state variables, are generally inferred from<br>
probe measurements using analytic or empirical formulas derived by applying different<br>
approximations to physical theories. The objective of this work is to develop more<br>
accurate techniques to infer physical parameters from probe measurements under<br>
more realistic plasma conditions with quantifiable uncertainties and with a potential<br>
for incremental improvements by adding more complex physical processes. Threedimensional<br>
particle-in-cell simulations are used to calculate the current collected<br>
by fixed-bias spherical Langmuir probes relative to the satellite under conditions of<br>
increasing realism, starting with an isolated probe attached to a guard, to probes<br>
attached to a satellite. The advantage of fixed bias probes considered in cases studied<br>
throughout this thesis, is their higher temporal and spatial resolutions, compared<br>
to the more standard mode operation where bias voltages are swept in time. The<br>
calculated currents and the plasma and satellite parameters assumed in the simulations<br>
are then used to build a solution library or synthetic data set, to construct<br>
regression models to infer parameters of interest such as the satellite potential, the<br>
density, the plasma flow velocity, and the ratio between the plasma density and the<br>
square root of the temperature. The solution library is randomly split into two disjoint<br>
sets; one to train models, and the other to validate inferences, and assess the<br>
skill of the models and quantify their uncertainties. Different approaches are used to train models, including radial basis function (RBF) regressions, deep neural networks,<br>
and combinations of these methods with analytic estimates using the boosting<br>
ensemble learning technique, and affine transformations. In the combined approach,<br>
the parameters of interest are first estimated using analytic expressions, followed by<br>
regressions to reduce inference errors. Simple affine transformations are also applied<br>
to improve the accuracy of analytic inferences, when the Pearson correlation coefficient<br>
R with known values is high. In each case, models’ inference skills are assessed<br>
using different metrics to quantify discrepancies compared with known values in synthetic<br>
data sets. The models show excellent performance with the maximum relative<br>
error ranging from 7% to 12% for the density and the ratio of density and the square<br>
root of temperature, and a maximum absolute error in the range of 0.2 V to 0.4 V for<br>
the floating potential in all the cases considered. The models are applied to in situ<br>
data, and the inferences are compared to in-situ data from satellites, with which they<br>
show excellent qualitative agreement. Finally, the agreements between the models<br>
inferences and the synthetic data values indicate that the approaches used in this<br>
thesis are promising with the advantage of producing uncertainty margins that are<br>
specifically related to the inference techniques used.</p>