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The story of fusion has always been about creating clean and reliable energy. However, the key to making it real may be less than data about magnets and plasmas – how they are generated, simulated and interpreted. Each experiment produces massive quantities of it: terabytes of plasma readings, maps of magnetic fields and heat flow measurements. This is a flood too powerful to process older models. And translating that into understanding is starting to feel more like an AI problem than a physics one.
That’s what the new partnership between DeepMind and Commonwealth Fusion Systems (CFS) is all about. CFS, the MIT spinoff that is developing the Compact Spark Reactor, hopes to show that controlled fusion can, eventually, produce much more energy than that. DeepMind’s job is to work toward making that vision a reality—not by building hardware, but by training machines to read, predict, and control what’s happening inside a tiny fusion core.
The focal point of the collaboration is Torx, a different physics simulator developed by DeepMind, and a set of reinforcement learning models that learn from simulated plasma data. Together, they develop a closed-loop system that trains using simulated simulations that can be generated at scale: predicting how the plasma will behave, determining what adjustments keep it stable and feeding that knowledge back into the CFS experiments. It is, simply put, an AI control architecture designed to maintain plasma stability – something no fusion reactor has maintained long enough to achieve net energy.
It all comes down to control. Controlling plasma is trying to control liquid electricity. Each magnetic pulse or temperature change sends shock waves through dozens of other variables, creating a network of feedback loops that combine with breathtaking complexity and speed — too fast for any human to track in real time. The challenge for DeepMind is to harness the chaos—translating raw sensor data into structured signals that a machine can respond to faster than any engineer.
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Torax simulates synthetic datasets that illustrate how plasma can behave in millions of possible configurations. Reinforcement learning models then sift through this data, looking for combinations that keep Spark’s plasma balanced and productive.
As actual sensor data begins to arrive, the system will begin to learn what actually happened with its predictions. Model and machine evolve simultaneously over many runs – an adaptive data system that not only describes fusion, but learns to live it.
“Torx is an advanced, open-source plasma simulator in the professional space and saved a lot of human effort in creating and maintaining our simulation environment for SPARK,” says Devon Battaglia, senior manager of physics operations at CFS. “It is now an essential aspect of our work to understand how the plasma will behave under different conditions.”
According to DeepMind, “The integration of our AI technologies with CFS’s advanced experimental hardware is a natural and exciting collaboration that we hope will open new opportunities for science.”
But there is another level to this story. It’s not just about getting fusion to work — it’s also about the growing overlap of AI and energy. As models grow and data centers guzzle more power, tech companies are dreading the days of incremental advances. They are thinking about long-term energy supply.
Google, the parent company behind DeepMind, invested in CFSK’s 63,863 million Series B2 round and agreed to purchase 200 megawatts of power under a future PPA from its first commercial fusion plant in Virginia. DeepMind’s fusion research does not exist in a bubble. It builds on Google’s broader initiative to power its infrastructure with carbon-free energy.
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And technically, it makes sense. Fusion reactors are among the most complex machines ever built by humans. Thousands of variables – magnetic fields, fuel injection and exhaust, plasma density – can be controlled but constantly and unpredictably interact. Engineers say there are “too many knobs for humans to turn.” It was designed specifically to solve this kind of problem—reinforcement learning: a system that learns—and learns—and learns by running millions of simulated scenarios until it gets a chance to work.
“Using Tauras in conjunction with reinforcement learning or evolutionary search methods such as AlphaAlphavolo, our AI agents can explore a wide number of possible operating scenarios in simulation, rapidly identifying the most efficient and robust paths to generating clean energy.” “This can help CFS focus on the most promising strategies, increasing the chances of success from day one, before Spark is fully commissioned and operational at full power.”
When running at full power, the spark will generate extraordinary heat in a small volume just off its inner wall. Keeping a lid on the path from this model requires magnetic adjustments in milliseconds – which is what DeepMind’s AI agents are now being taught to do. Early simulations show that they can learn to spread the heat load across the reactor’s inner wall or diverter, helping the material stay within safe thermal limits.
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While previous simulators were written in older languages, Torx is coded in Jax and runs on top of a GPU – the same hardware that powers modern AI models. This means it can conduct millions of fast, differential simulations in parallel, integrating high-energy physics with the computing infrastructure that already powers today’s machine learning research.
DeepMind’s team says this is just the beginning. “We’re laying the groundwork for AI to be the intelligent, adaptive system at the heart of the future fusion power plant.” If this vision pans out, fusion reactors can no longer rely on physicists turning knobs—they can act like self-improving software, constantly recovering based on new data, learning with every pulse, and moving fusion science closer to becoming an energy reality.
This article was first published Big Datawire.
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