Controlling Nuclear Fusion Plasma with AI
Scientists have been searching for a clean and limitless energy source to solve the global energy crisis. One promising contender is nuclear fusion, the same reaction that powers stars. By fusing hydrogen, researchers can release massive amounts of energy. To recreate the extreme conditions needed for nuclear fusion here on Earth, scientists use a device called a tokamak. However, the plasma in these machines is unstable, making it difficult to sustain nuclear fusion. DeepMind, in collaboration with the Swiss Plasma Center at EPFL, has developed a deep reinforcement learning system to autonomously control the plasma in a tokamak.
Using a combination of deep reinforcement learning and a simulated environment, the team at DeepMind successfully controlled the nuclear fusion plasma in the Variable Configuration Tokamak (TCV) in Lausanne, Switzerland. The system not only kept the plasma steady but also sculpted it into different shapes. This breakthrough allows scientists to study how the plasma reacts under different conditions, advancing our understanding of fusion reactors.
This work showcases the potential of machine learning and collaboration in accelerating scientific discovery. DeepMind’s approach is not limited to nuclear fusion. They are also applying it to fields like quantum chemistry, material design, weather forecasting, and more to solve fundamental problems and benefit humanity.
The Challenges in Nuclear Fusion Research
Research into nuclear fusion is currently limited by the availability of expensive tokamaks, which are in high demand. Additionally, each experiment on a tokamak only lasts a few seconds, followed by a long cooldown period. To overcome these limitations, researchers have turned to simulators to advance their research. DeepMind used simulation tools developed by EPFL to train their reinforcement learning system before validating the results on the real TCV.
Achieving Success with a Simple and Flexible Approach
Existing plasma-control systems are complex, requiring separate controllers for each magnetic coil in the tokamak. DeepMind’s architecture, on the other hand, uses a single neural network to control all the coils simultaneously. The system learns the best voltage settings directly from sensor data, resulting in precise control over the plasma.
The Future of Fusion and Beyond
DeepMind’s successful demonstration of tokamak control highlights the potential of AI in accelerating fusion science. This technology could be used to design new tokamaks and their controllers simultaneously. The team believes that reinforcement learning will transform industrial and scientific control applications, from energy efficiency to personalized medicine. The future looks bright for AI’s role in complex machine control and problem-solving.