AI revolutionizes fusion power energy by enhancing control, optimization, and efficiency in various aspects of the fusion process. It aids in real-time monitoring, precise adjustments, and predictive modeling, leading to improved reactor performance and increased energy output. Additionally, AI facilitates advanced data analysis, helping researchers identify optimal conditions for sustained fusion reactions. Overall, the integration of AI in fusion power contributes to overcoming complex challenges, accelerating research, and ultimately making fusion energy more viable and sustainable.
By enhancing plasma control, magnetic confinement, automated experiment design, materials discovery and optimization, predictive maintenance and fault detection, scenario modeling and risk analysis, control of power systems and grid integration, advanced diagnostics and data analysis, and predictive maintenance and fault detection, artificial intelligence (AI) has the potential to completely transform fusion power energy. Artificial intelligence (AI) algorithms can help with experiment design and planning, improve magnetic confinement design, and increase real-time control of plasma characteristics. Better plasma stability and confinement can result from the optimization of magnetic confinement systems using machine learning approaches. AI-driven scenario modeling enhances decision-making, maximizes power system integration, and evaluates the impact of external and operational factors on reactor performance. Refinement of reactor design and operation models may be achieved by using advanced diagnostics and data analysis to uncover insights on plasma behavior and reactor performance.
https://nucleus.iaea.org/sites/fusionportal/Shared%20Documents/PC_8663_Fusion_Competition.pdf
https://www-pub.iaea.org/MTCD/publications/PDF/PUB1945_web.pdf
https://science.osti.gov/-/media/fes/fesac/pdf/2018/TEC_Report_15Feb2018.pdf
https://discovery.ucl.ac.uk/id/eprint/10175865/1/2022_Review_of_Data-Driven_Plasma_Science.pdf
https://www.researchgate.net/publication/341340176_Machine_learning_for_predictive_scheduling_and_resource_allocation_in_large_scale_manufacturing_systems
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