7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Real-time predictions of ICRF power absorption profiles via machine learning
Á. Sánchez-Villar1, Z. Bai2, N. Bertelli1, E. W. Bethel3, D. Corona1, J. Hillairet4,T. Perciano2, S. Shiraiwa1, G. M. Wallace5, and J. C. Wright5
1
Princeton Plasma Physics Laboratory, 2 Lawrence Berkeley National Laboratory,
3San Francisco State University, 4 CEA Cadarache, 5 MIT Plasma Science and Fusion Centere-mail (speaker): asanche2@pppl.gov
Significant acceleration of accurate radio-frequency(RF) power absorption profile predictions in the ion
cyclotron range of frequencies (ICRF) was achieved
using machine learning (ML)-based algorithms. A
traditional approach for obtaining these predictions is to
use the spectral based full wave solver TORIC [1,2].
However, the considerable computational expenses ofthese codes have impeded their application to real-time
control, inter-shot predictive modeling or specificscenario optimization. The physics of ICRF heating
features complex power absorption phenomenology asboth electron and ion species are contributing [3]. As
each plasma-wave interaction yields a power deposition
with different shapes, the resultant RF absorption
profile presents a significant challenge for training
ML-algorithms. In this work we present the results ofour ICRF wave surrogate modeling efforts to obtainaccelerated predictions of the power absorption profilesof WEST [4] and NSTX [5] without losing physics
fidelity. Two ML-methods are employed to develop thesurrogate models: the Random Forest Regressor (RFR)[6] and the Multi-Layer Perceptron (MLP) [7]. The
models are trained using databases generated withTORIC, which solves the wave equation in bothminority and high-harmonic fast wave (HHFW) heating
schemes, present in WEST and NSTX, respectively.
Over 104 cases are sampled in each machine-specificparametric regime of interest via the Latin Hypercube
Sampling method [8], which produces a pseudo-random
sample optimally distributed over the multidimensional
input parameter space. The accuracy (scoring) achievedby the surrogate models is measured in terms of averagemean squared error (MSE) and coefficient ofdetermination (R2). Best scorings achieved vary within[~10-2,~10-5] MSE and [0.71-0.96], respectively. The
surrogates implemented for the 1D RF power
absorption of electrons and ion species (i.e. hydrogenand deuterium ions) reduce the inference time fromO(~1-5min) to O(~30-50μs). While the MLP canachieve better performances, its scoring is strongly
sensitive to the presence of outliers, which are found in
the TORIC generated database, and result in decreased
MLP scoring due to increased noise levels in thepredictions. Further discussion on the nature of theseoutliers is provided. In the HHFW database, outlier
filtering provides a significant boost to surrogate
scoring, where, for instance, electron power predictions
can improve R2 from 0.62-0.70 to 0.95-0.96 and MSEfrom 2.1-2.6⋅10-3 to 2.1-1.1⋅10-5 for RFR-MLP
respectively. Power absorption profile predictions of theoutlier cases with filtered-dataset trained RFR preserve
the main physical aspects of ICRF absorption whileeliminating outlier features (see Figure 1). Althoughoutliers are also present in the WEST database, its
origin is shown to be correlated to underresolved small
wavelength IBW modes in the high field side.
Application of principal component analysis allowed
to reduce the dimensionality of electron and hydrogen
power absorption profiles for WEST database, from 300
point profiles to uniquely 5 and 7 components,respectively. Training MLP regressors on the projected
data resulted in boosting both electron and hydrogen
power predictions scoring from the R2=0.57-0.64
obtained using the original data to R2=0.85-0.85.
Finally, results of the application of both RFRs andMLPs to predict 2D RF variables such as the power
deposition and the wave electric field will be discussed.
This work was supported by the U.S. Department ofEnergy under contract number DE-AC02-09CH1146.

Figure 1: Prediction of electron power absorption profile for an
outlier case in the HHFW database

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