Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma
Azarakhsh Jalalvand, Joseph Abbate, Rory Conlin, Geert Verdoolaege, Egemen Kolemen · IEEE Transactions on Neural Networks and Learning Systems · 2021
Nuclear fusion is a promising alternative to address the problem of sustainable energy production. The tokamak is an approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. We study the characteristics and optimization of reservoir computing (RC) for real-time and adaptive prediction of plasma profiles in the DIII-D tokamak. Our experiments demonstrate that RC achieves comparable results to state-of-the-art (deep) convolutional neural networks (CNNs) and long short-term memory (LSTM) models, with a significantly easier and faster training procedure. This efficient approach allows for fast and frequent adaptation of the model to new situations, such as changing plasma conditions or different fusion devices.