Application of interpretable machine learning for cross-diagnostic inference on the ST40 spherical tokamak

TL;DR

This paper introduces a method to transform black-box machine learning models into interpretable grey-box models, demonstrated on plasma diagnostics for predicting electron temperature and density profiles from soft X-ray data.

Contribution

The paper presents a novel approach to parameterize black-box models into grey-box models, enhancing interpretability in plasma diagnostics applications.

Findings

Grey-box models match black-box predictions across plasma conditions.

The approach improves interpretability without sacrificing accuracy.

Model-agnostic method enables broad application across architectures.

Abstract

Machine learning models are exceptionally effective in capturing complex non-linear relationships of high-dimensional datasets and making accurate predictions. However, their intrinsic ``black-box'' nature makes it difficult to interpret them or guarantee ``safe behavior'' when deployed in high-risk applications such as feedback control, healthcare and finance. This drawback acts as a significant barrier to their wider application across many scientific and industrial domains where the interpretability of the model predictions is as important as accuracy. Leveraging the latest developments in interpretable machine learning, we develop a method to parameterise ``black-box'' models, effectively transforming them into ``grey-box'' models. We apply this approach to plasma diagnostics by creating a parameterised synthetic Soft X-Ray imaging $-$ Thomson Scattering diagnostic, which predicts high temporal resolution electron temperature and density profiles from the measured soft X-ray emission. The ``grey-box'' model predictions are benchmarked against the trained ``black-box'' models as well as a diverse range of plasma conditions. Our model-agnostic approach can be applied to various machine learning architectures, enabling direct comparisons of model interpretations.