@inproceedings{e365b4bbbc8e4fa991266e728a72dc1e,
title = "Multimodal Prediction of Tearing Instabilities in a Tokamak",
abstract = "Tokamak is a torus-shaped nuclear fusion device that uses magnetic fields to confine fusion fuel in the form of plasma. Tearing instability in plasma is a major issue in which the magnetic field breaks and recombines in tokamak. This instability can lead to plasma disruption that terminates the fusion power generation and damages the plasma-facing wall materials. For a successful steady operation of a large-scale tokamak without disruption, it is required to predict and alarm the tearing instabilities well in advance to avoid them. In this work, we develop and validate a deep neural network-based multimodal prediction system that estimates the future tearing instability likelihood from multi-diagnostics signals in the DIII-D tokamak.",
keywords = "deep neural network, multimodal prediction, nuclear fusion, tearing instability, tokamak",
author = "Jaemin Seo and Rory Conlin and Andrew Rothstein and Kim, {Sang Kyeun} and Joseph Abbate and Azarakhsh Jalalvand and Egemen Kolemen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Joint Conference on Neural Networks, IJCNN 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/IJCNN54540.2023.10191359",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings",
address = "United States",
}