Data-driven models for Alfvén eigenmode classification based on high resolution ECE diagnostics at DIII-D

Modern day tokamaks have made significant advances in fusion understanding, but further progress towards steady-state operation is challenged by a host of kinetic and MHD instabilities. Alfvén eigenmodes (AE) are a class of mixed kinetic and MHD instabilities that are important to identify and control because they can reduce confinement and potentially damage tokamak components. In the present work, we utilize an expert-labeled database of DIII-D discharges and use (deep) recurrent neural networks such as a Reservoir Computing Network (RCN) to classify five AE modes, namely, BAE, EAE, LFM, RSAE and TAE. To deploy the model on a high-throughput FPGA accelerator for integration in the real-time plasma control system, we consider a data processing pipeline with minimum complexity. We trained a simple yet effective RCN on 40 raw ECE diagnostics down-sampled from 500kHz to only 2kHz. Our preliminary results show that such a model achieves a True Positive Rate of 91% with only 7% False Positive Rate, indicating promise for future investigation of AE modes such as detecting shape and location of these instabilities inside plasma and consolidation of the model into a real-time control strategy .