Abstract Details
Abstracts
Author: Matt Landreman
Requested Type: Consider for Invited
Submitted: 2025-02-21 08:11:30
Co-authors: J Y Choi, C Alves, P Balaprakash, R M Churchill, R Conlin, G Roberg-Clark
Contact Info:
University of Maryland
8223 Paint Branch Dr
College Park, MD 20740
USA
Abstract Text:
Magnetic geometry has a significant effect on turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods and a dataset of > 200,000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. At fixed gradients, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the geometries with particularly high or low heat flux. Interpretable regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection, and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, openly available online, may also be used to test other proposed surrogates; we find many previously published proxies do correlate well with both the heat flux and stability boundary.
Characterization: 6.0
Comments: