Summary: The web content discusses the challenges in understanding and controlling fusion plasmas due to their complex nature and limited diagnostic capabilities. The use of machine learning is proposed to improve super-resolution diagnostics, predict plasma behavior, detect instabilities, and generate synthetic diagnostic signals. The content also focuses on validating tokamak transport models and achieving stable divertor radiation detachment in a cost-effective manner using real-time carbon-III emission proxies. The innovative approach aims to revolutionize fusion plasma diagnostics and control strategies with broader applications in various scientific fields.
https://control.princeton.edu/machine-learning-for-rt-profile-control-in-tokamaks/