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 …

A fast and accurate turbulence transport model based on quasilinear gyrokinetics is
developed. The model consists of a set of neural networks trained on a bespoke quasilinear …

Abstract Model-based plasma scenario development lies at the heart of the design and
operation of future fusion powerplants. Including turbulent transport in integrated models is …

Rapid inter-discharge simulation and optimization using the RAPTOR code have allowed
the development of a reliable and reproducible early heating strategy for an advanced …

The advent of machine learning (ML) has revolutionized the research of plasma
confinement, offering new avenues for exploration. It enables the construction of models that …

We discuss how the combination of experimental observations and rapid modeling has
enabled to improve understanding of the tokamak ramp-down phase in ASDEX Upgrade. A …

Scenario development in tokamaks is an open area of investigation that can be approached
in a variety of different ways. Experimental trial and error has been the traditional method …

An optimized plasma current ramp-down strategy is critical for safe and fast termination of
plasma discharges in a tokamak demonstration fusion reactor (DEMO), both in planned and …

Estimation of the dynamic evolution of the electron plasma density during a tokamak
discharge is crucial since it directly affects the plasma performance, confinement and …

A machine learning‐based semi‐empirical turbulent transport model DeKANIS has been
modified to apply it independently of the device. DeKANIS predicts particle and heat fluxes …