@inproceedings{0ff19887ab944d65ac9fd33cc1859bec,
title = "ICRF wave propagation and absorption modelling via machine learning",
abstract = "A surrogate model of the wave absorption in the ion cyclotron range of frequencies is presented. The model is trained to capture the physics of 1D electron and ion power absorption profiles for both the high harmonic fast wave scheme in NSTX, and the minority heating scheme in WEST. The surrogate models, based on both the random forest regressor and the multilayer perceptron algorithms, reduce inference time of 1D power absorption profiles from 1-5 minutes required by TORIC to ∼50 µs with high accuracy (i.e. R2 = 0.71−0.96).",
author = "S{\'a}nchez-Villar and Z. Bai and N. Bertelli and Bethel, \{E. W.\} and T. Perciano and S. Shiraiwa and G. Wallace and Wright, \{J. C.\}",
note = "Publisher Copyright: {\textcopyright} 2023 49th EPS Conference on Plasma Physics, EPS 2023. All rights reserved.; 49th EPS Conference on Plasma Physics, EPS 2023 ; Conference date: 03-07-2023 Through 07-07-2023",
year = "2023",
language = "English (US)",
series = "49th EPS Conference on Plasma Physics, EPS 2023",
publisher = "European Physical Society (EPS)",
booktitle = "49th EPS Conference on Plasma Physics, EPS 2023",
}