FES Highlights

The Diag2Diag AI model produces synthetic super-resolution data to build a deeper understanding of fusion devices.

Scientists found a potential way to suppress large damaging edge-localized modes, providing an approach to protect future devices.

The Uncertainty Toolbox, a popular open-source library for uncertainty quantification and calibration, is a valuable tool for fusion and other research.

Negative triangularity exhibits high core fusion performance and good power handling, pointing to a compelling approach for future fusion pilot plants.

Researchers validate a new workflow for plasma transport models, aiding future fusion device design.

Public researchers partner with a private company to improve simulations key to controlling plasma heat in a fusion energy power plant.

Researchers trained a deep reinforcement learning algorithm to adjust magnetic confinement fields in real time to maintain plasma stability.

A new quantum algorithm speeds up simulations of coupled oscillators dynamics.

Integrating machine learning with real-time adaptive control produces high-performance plasmas without edge instabilities, a key for future fusion reactors.

Study finds that neutral beam performance can be experimentally deduced from electron temperature evolution during neutral beam injection.