Resistive Wall Mode stability forecasting in NSTX through Balanced Random Forests and counterfactual explanations*

Recent progress in the Disruption Event Characterization and Forecasting (DECAF) framework [1] has shown that Machine Learning (ML) guided by physics theory [2] can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized Random Forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict Resistive Wall Mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on bootstrap samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model's decisions, a diverse set of counterfactual explanations based

on determinantal point processes (DPP) [3] is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical slowing magnetohydrodinamic (MHD) activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics knowledge, it motivates the usage of counterfactuals to simulate real-time control by generating the β_N levels that would have kept the RWM stable for a set of unstable discharges. 

[1] J.W. Berkery et al., Physics of Plasmas 24, 056103 (2017)

[2] A. Piccione et al., Nuclear Fusion 60, 046033 (2020)

[3] R.K. Mothilal et al., Proc. Conf. FAT ’20 (2020)