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.

References in corpus (3)

• A New Paradigm for Turbulent Transport Across a Steep Gradient in Toroidal Plasmas
• Verification and validation of gyrokinetic and kinetic-MHD simulations for internal kink instability in DIII-D tokamak
• Formulation and verification of multiscale gyrokinetic simulation of kinetic-MHD processes in toroidal plasmas

Cited by in corpus (4)

• 2022 Review of Data-Driven Plasma Science
• Reconstruction of tokamak plasma safety factor profile using deep learning
• Verification and validation of gyrokinetic and kinetic-MHD simulations for internal kink instability in DIII-D tokamak
• Physics-Informed Deep Learning Model for Line-integral Diagnostics Across Fusion Devices