An AI-based System to Assist Session Leader during Stellarator Operations - a Prototype

The advent of artificial intelligence [AI] has a deep impact on numerous scientific and industrial fields, particularly in magnetic confinement fusion. This work explores the application of AI techniques to help scientists with the design of future fusion experiments based on previous experimental campaigns. Traditional ways of interpreting and designing fusion discharges often require extensive computational resources, time, and research experience (including trial and error procedure). By leveraging AI, it is shown the possibility to partially overcome these constraints. As an example, the explored AI techniques are applied to the TJ-II stellarator. The major goal of this work is the development of an AI system that is able to estimate the operation parameters given a desired plasma scenario. The latter will be termined by the magnetic fluctuations measured by a Mirnov coil and the produced operation parameters are the plasma fueling and heating configurations. The results indicate that AI can approximate fusion experiments and assist scientists for the design of new ones, offering a faster and cost-effective alternative to conventional approaches. This study paves the way for more efficient research and development processes in fusion experiments, with AI serving as a tool for innovation and discovery.