Application of neural networks in beam emission spectroscopy modelling

Beam emission spectroscopy (BES) is an active plasma diagnostic utilized for plasma density measurements. BES synthetic diagnostics are computationally expensive and comprehensive modelling suites designed to provide a better understanding of the diagnostics' perception of underlying plasma phenomena. RENATE-OD is an advanced BES synthetic diagnostic relying on a rate-equation solver to derive the beam emission for given input plasma profiles. In this work, linear regression, multi-layer perceptron and extreme learning machines were explored as a substitute for the rate-equation solver to predict the beam emission. Our experiments show that extreme learning machines are suitable for the task of predicting the arising emission profiles with very high accuracy and about 8000 times faster than RENATE-OD.