Feedforward beta control in the KSTAR tokamak by deep reinforcement learning

Development of integrated suite of codes and its validation on KSTAR

(2021) C.Y. Lee et al.   NUCLEAR FUSION

Machine learning control for disruption and tearing mode avoidance

(2020) Yichen Fu et al.   PHYSICS OF PLASMAS

Fast modeling of turbulent transport in fusion plasmas using neural networks

(2020) K. L. van de Plassche et al.   PHYSICS OF PLASMAS

On hybrid Scenarios in KSTAR

(2020) Yong-Su Na et al.   NUCLEAR FUSION

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

(2019) Julian Kates-Harbeck et al.   NATURE

Safety factor profile control with reduced central solenoid flux consumption during plasma current ramp-up phase using a reinforcement learning technique

(2019) T. Wakatsuki et al.   NUCLEAR FUSION

Deep neural network Grad–Shafranov solver constrained with measured magnetic signals

(2019) Semin Joung et al.   NUCLEAR FUSION

Feasibility experiment of physics-based global electron temperature profile control in KSTAR

(2018) H.-S. Kim et al.   FUSION ENGINEERING AND DESIGN

Non-inductive improved H-mode operation at ASDEX Upgrade

(2017) A. Bock et al.   NUCLEAR FUSION

Self-consistent core-pedestal transport simulations with neural network accelerated models

(2017) O. Meneghini et al.   NUCLEAR FUSION

Effect of electron cyclotron beam width to neoclassical tearing mode stabilization by minimum seeking control in ITER

(2017) Minho Park et al.   NUCLEAR FUSION

Formation of the internal transport barrier in KSTAR

(2017) J. Chung et al.   NUCLEAR FUSION

Chapter 4: Advanced Tokamak Scenario Development at JET

(2017) C. Gormezano et al.   FUSION SCIENCE AND TECHNOLOGY

The direct criterion of Newcomb for the ideal MHD stability of an axisymmetric toroidal plasma

(2016) A. H. Glasser   PHYSICS OF PLASMAS

Demonstration of real-time control for poloidal beta in KSTAR

(2015) Hyunsun Han et al.   FUSION ENGINEERING AND DESIGN

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

High-beta, steady-state hybrid scenario on DIII-D

(2015) C.C. Petty et al.   NUCLEAR FUSION

Numerical study on neoclassical tearing mode stabilization via minimum seeking method for the island width growth rate

(2015) M. Kim et al.   NUCLEAR FUSION

Time-dependent simulations of feedback stabilization of neoclassical tearing modes in KSTAR plasmas

(2014) Kyungjin Kim et al.   FUSION ENGINEERING AND DESIGN

Amplitude based feedback control for NTM stabilisation at ASDEX Upgrade

(2014) Christopher Rapson et al.   FUSION ENGINEERING AND DESIGN

Modeling of transport phenomena in tokamak plasmas with neural networks

(2014) O. Meneghini et al.   PHYSICS OF PLASMAS

Development of advanced inductive scenarios for ITER

(2013) T.C. Luce et al.   NUCLEAR FUSION

Improved confinement in JET hybrid discharges

(2012) J Hobirk et al.   PLASMA PHYSICS AND CONTROLLED FUSION

Electron cyclotron emission diagnostics on KSTAR tokamak

(2010) S. H. Jeong et al.   REVIEW OF SCIENTIFIC INSTRUMENTS

Development and validation of a predictive model for the pedestal height

(2009) P. B. Snyder et al.   PHYSICS OF PLASMAS