Investigating characteristics of the growth rates from QuaLiKiz using an interpretable surrogate model

Journal article, 2025

We present an interpretable, machine learning-based surrogate model for the eigenvalue solver in QuaLiKiz, a model that simulates turbulent transport in fusion plasmas. The aim is to exploit prediction transparency to gain insight into the anticipated behavior of QuaLiKiz-based surrogates and the underlying eigenvalue solver, a task that is more challenging when using black-box surrogate models. Specifically, we focus on predicting the growth rate of turbulence driving ion temperature gradient instabilities computed by QuaLiKiz for the normalized poloidal wavenumber k(theta)rho(s) = 0.325 . We split the task into a classification task, to determine whether the growth rate is positive (unstable mode) or not, and a growth rate prediction task, knowing the mode is unstable. The dataset used is a QuaLiKiz dataset based on JET pulses. The method used is the NeuralBranch method, a neural network-based method that reveals how the inputs of the models, in this case plasma parameters, impact the output. Results show that NeuralBranch models outperform linear models and match dense neural networks (traditional black-box models) in accuracy while being interpretable. By analyzing the NeuralBranch models, we identify parameter dependencies that cannot be captured by linear models. For instance, the models indicate that the stabilizing effect of ExB shear on the growth rate is suppressed at low magnetic shear, which can be attributed to how ExB shear influences the eigenfunction width in QuaLiKiz. In summary, this work demonstrates how interpretable methods can shed light on the behavior of surrogates and their underlying counterpart, thus enhancing both model credibility and understanding. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).