[HTML][HTML] Magnetic control of tokamak plasmas through deep reinforcement learning
Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a
promising path towards sustainable energy. A core challenge is to shape and maintain a …
promising path towards sustainable energy. A core challenge is to shape and maintain a …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Machine learning and Bayesian inference in nuclear fusion research: an overview
This article reviews applications of Bayesian inference and machine learning (ML) in
nuclear fusion research. Current and next-generation nuclear fusion experiments require …
nuclear fusion research. Current and next-generation nuclear fusion experiments require …
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
Online tuning of particle accelerators is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …
require manual intervention by experienced human operators. Autonomous tuning is a …
Avoiding fusion plasma tearing instability with deep reinforcement learning
For stable and efficient fusion energy production using a tokamak reactor, it is essential to
maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is …
maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is …
Solving real-world optimization tasks using physics-informed neural computing
J Seo - Scientific Reports, 2024 - nature.com
Optimization tasks are essential in modern engineering fields such as chip design,
spacecraft trajectory determination, and reactor scenario development. Recently, machine …
spacecraft trajectory determination, and reactor scenario development. Recently, machine …
PID-inspired inductive biases for deep reinforcement learning in partially observable control tasks
Deep reinforcement learning (RL) has shown immense potential for learning to control
systems through data alone. However, one challenge deep RL faces is that the full state of …
systems through data alone. However, one challenge deep RL faces is that the full state of …
Development of an operation trajectory design algorithm for control of multiple 0D parameters using deep reinforcement learning in KSTAR
This work develops an artificially intelligent (AI) tokamak operation design algorithm that
provides an adequate operation trajectory to control multiple plasma parameters …
provides an adequate operation trajectory to control multiple plasma parameters …
A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels
A fundamental issue in the hydraulics of movable bed channels is the measurement of
friction factor (λ), which represents the head loss because of hydraulic resistance. The …
friction factor (λ), which represents the head loss because of hydraulic resistance. The …
Offline model-based reinforcement learning for tokamak control
Control for tokamaks, the leading candidate technology for nuclear fusion, is an important
pursuit since the realization of nuclear fusion as an energy source would result in virtually …
pursuit since the realization of nuclear fusion as an energy source would result in virtually …