The design of fusion reactors necessitates a comprehensive assessment of interacting components to ensure engineering and economic feasibility. Complex systems, like fusion power plants, are often preliminarily modeled using low - fidelity ‘systems codes’ for efficient design space exploration.

This research investigates applying aerospace-derived multi-fidelity techniques to incorporate higher-fidelity models while expediting system convergence and optimizing plant design. Multi-fidelity analysis utilizes high-fidelity models to locally calibrate low-fidelity models. We automate the integration of high-fidelity and low-fidelity models using Gaussian process regression, a Bayesian machine learning method. Low-fidelity power law models are ‘trained’ using high-fidelity models. Gaussian Process Regression is used to estimate the error of the low-fidelity model as design points vary from the initial training data set. If the error is large for a given design point, the point is added to the training set for the high-fidelity model, and the system is re-evaluated.

It was found that Gaussian Process Regression as used in this algorithm offers greater advantages at higher fractional uncertainties (148 high-fidelity model evaluations saved after 300 total points requested when u = 0.3) while being less advantageous at lower fractional uncertainties (5 highfidelity model evaluations saved after 300 total points requested when u = 0.1). It was additionally observed that constricting input parameters to smaller value ranges resulted in more saved high – fidelity model evaluations compared to previous tests (155 high-fidelity model evaluations saved after 300 total points requested when u = 0.3).