Deep learning based surrogate model for first-principles global simulations of fusion plasmas
Abstract
The accurate identification and control of plasma instabilities is important for successful fusion experiments. First-principles simulations which can provide physics based instability information including the growth rate and mode structure are generally not fast enough for real-time applications. In this work, a deep-learning based surrogate model as an instability simulator has been developed and trained in a supervised manner with data from the gyrokinetic toroidal code (GTC) global electromagnetic simulations of the current driven kink instabilities in DIII-D plasmas. The inference time of the surrogate model of GTC (SGTC) is on the order of milliseconds, which fits the requirement of the DIII-D real-time plasma control system (PCS). SGTC demonstrates strong predictive capabilities for the kink mode instability properties including the growth rate and mode structure.
Keywords
Cite
@article{arxiv.2106.10849,
title = {Deep learning based surrogate model for first-principles global simulations of fusion plasmas},
author = {Ge Dong and Xishuo Wei and Jian Bao and Guillaume Brochard and Zhihong Lin and William Tang},
journal= {arXiv preprint arXiv:2106.10849},
year = {2021}
}