Machine learning for solving the inverse thermal problem on STRIKE in real-time

The short time retractable instrumented calorimeter experiment is a critical diagnostic tool for characterizing the source for production of negative ions of deuterium extracted from radio frequency plasma. Due to technical limitations, direct temperature measurement on the calorimeter’s front side, facing the ion source, is infeasible. Instead, reconstructing the front-side heat flux from back-side temperature data formulates an inverse problem. This study introduces a convolutional neural network (CNN) to achieve real-time reconstruction of the heat f lux distribution, modeled with Gaussian fitting for each beamlet, directly from infrared (IR) camera images. The CNN’s performance is benchmarked against a multi-layer perceptron (MLP) model, which requires offline parametrization of IR images, limiting real-time applicability. Using experimental IR images and heat flux data generated via iterative finite element method modelling, the CNN demonstrated comparable accuracy to the MLP without the need for feature extraction engineering, offering a robust real-time solution.