Filling in the Gaps: AI for Super-Resolution and Diagnostic Recovery
The Diag2Diag AI model produces synthetic super-resolution data to build a deeper understanding of fusion devices.
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.
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.
Researchers trained a deep reinforcement learning algorithm to adjust magnetic confinement fields in real time to maintain plasma stability.
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.
The first measurement of ion temperature in magnetic islands identified a steep gradient, providing insights for improving plasma confinement in tokamaks.
By achieving very high density and confinement quality at the same time, researchers make new strides toward fusion energy.
Plasmas with negative triangularity show reduced gradients that develop into instabilities, including under conditions relevant to fusion power plants.
Perturbing the edge magnetic field of a tokamak produces a counterintuitive response: particles entering the confined region rather than escaping it.
For the first time, scientists successfully track energetic ion flow through space and energy driven by electromagnetic waves in fusion plasmas.