Magnetically confined fusion experiments face fundamental challenges in controlling high-temperature plasma instabilities. Recent trials demonstrate that deep reinforcement learning algorithms can adjust magnetic coil currents in sub-millisecond intervals to prevent disruptions.
Tearing Mode Instability Mitigation
By anticipating magnetic island formation, autonomous control systems maintain optimal plasma shaping under varying pressure profiles. This approach drastically reduces physical damage to reactor vessel walls.
Scaling to Net Energy Gain Reactors
Automated plasma control provides a scalable framework for next-generation tokamaks. Sustained plasma stability is essential for achieving continuous power production and commercial feasibility.