Affordable Access

deepdyve-link
Publisher Website

Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma.

Authors
  • Jalalvand, Azarakhsh
  • Abbate, Joseph
  • Conlin, Rory
  • Verdoolaege, Geert
  • Kolemen, Egemen
Type
Published Article
Journal
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Jun 01, 2022
Volume
33
Issue
6
Pages
2630–2641
Identifiers
DOI: 10.1109/TNNLS.2021.3085504
PMID: 34115598
Source
Medline
Language
English
License
Unknown

Abstract

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.

Report this publication

Statistics

Seen <100 times