arXiv:2605.05857v2 Announce Type: replace Abstract: Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challenge is controlling the plasma rotation profile, which strongly influences stability, confinement, and transport. While the average rotation can be controlled, controlling the full profile is challenging due to high dimensionality, response to multiple actuators and dependence on plasma condition. Learning-based control methods, such as reinforcement learning (RL), pr
The increasing maturity of AI, particularly reinforcement learning, is enabling its application to complex control problems in critical energy research fields like fusion.
Achieving practical fusion energy is a grand challenge with profound implications for global energy security and climate change mitigation, and AI can accelerate this development.
The application of advanced AI to highly complex and previously intractable control problems in fusion energy research has improved the efficiency and potential viability of fusion reactors.
- · Fusion energy researchers
- · AI/ML developers
- · Energy utilities
- · Governments investing in clean energy
- · Fossil fuel industry (long-term)
- · Traditional control systems developers
Improved stability and control in tokamak operations lead to faster progress in fusion energy development.
Accelerated fusion research could bring practical, clean, and abundant energy closer to reality, transforming the global energy landscape.
Widespread fusion power could trigger a new industrial revolution, reducing geopolitical competition over energy resources and enabling new scales of economic activity.
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