A data-driven approach to model calibration is developed to accurately obtain the input parameters for nonlinear dynamical systems. The paper focuses on the convergence properties of the proposed method, which play a significant role in understanding the validity and usefulness of any data-driven model. The input parameters of nonlinear dynamical systems are optimized to a reference solution, which can be experimental data or results from a high-fidelity computer simulation, using the Wasserstein metric and a phase-space representation of a set of time-dependent signals. Test cases shown in this paper include the Lorenz system and the discharge plasma of a Hall effect thruster to characterize the numerical uncertainties of the proposed data-driven approach, given a constructed reference solution. Distinct wells in the cost function, the Wasserstein metric, are obtained relative to the reference solution, illustrating the applicability of the proposed method to dynamical problems. The numerical uncertainties associated with the phase-space portrait and sampling time are discussed.

An extended Kalman filter (EKF) is developed to estimate unobserved states and parameters in plasma dynamical systems. Physical constraints are satisfied by adapting the process and measurement noise covariances to account for consistency between the estimates and the physical processes. First, the EKF is tested using the Lorenz system to demonstrate the robustness of the EKF with sparse measurement data. Then, the capabilities of the EKF are applied to investigate discharge current oscillations in a Hall effect thruster. It is demonstrated that the dynamics of the electron temperature can be estimated using the discharge current fluctuation as the measurement data. The propagation of the uncertainties of such estimates is also quantified.

A physically-constrained extended Kalman filter (EKF) is applied to various zero-dimensional global models for the estimation of plasma properties using time-dependent experimental data such as the plasma density or ion flux. The capability of the EKF is demonstrated to estimate unknown system states simultaneously, such as reaction rate coefficients and the absorbed electron input power, which can be difficult, if not impossible, to measure experimentally. Global models accounting for pure argon reactions and argon-oxygen reactions are used in this work to demonstrate the ability of the filter to estimate dynamic and complex systems. The results obtained from the EKF plasma global model illustrate that model-data fusion techniques can be used to estimate plasma properties and processes for time-varying systems, such as pulsed discharges.

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