Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

This article reviews applications of Bayesian inference and machine learning (ML) in
nuclear fusion research. Current and next-generation nuclear fusion experiments require …

Multi-channel, nonlinear predictions of core temperature and density profiles are performed
for the SPARC tokamak (Creely et al 2020 J. Plasma Phys. 86 865860502) accounting for …

In this work, we address a new feedforward control scheme for the normalized beta (β N) in
tokamak plasmas, using the deep reinforcement learning (RL) technique. The deep RL …

SPARC is designed to be a high-field, medium-size tokamak aimed at achieving net energy
gain with ion cyclotron range-of-frequencies (ICRF) as its primary auxiliary heating …

The design of future fusion reactors and their operational scenarios requires an accurate
prediction of the plasma confinement. We have developed a new model that integrates …

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak
devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The …

A new, fully data-driven algorithm has been developed that uses a neural network to predict
plasma profiles on a scale of τ E into the future given an actuator trajectory and the plasma …

We present an ultrafast neural network model, QLKNN, which predicts core tokamak
transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 3× …

Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of
sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma …