The Growing Success and Potential of AI in Fusion

The Potential of Fusion

Fusion energy has long been one of science’s most ambitious goals, and it’s easy to see why. If it can be made reliable and economical, it offers a path to abundant, clean electricity that strengthens both climate goals and energy security. The European Commission's Energy 2050 Roadmap highlights five scenarios achieving an 80% reduction in greenhouse gas emissions through nuclear energy, renewables, and carbon capture and storage, highlighting nuclear power as key to this energy transition [1]. While renewable sources of energy, such as wind, solar and hydroelectric, are important for supporting the transition from fossil fuels, they are simply not enough by themselves. The intermittent nature of these energy production processes is a problem, and the availability of land also limits renewables [2]. 

Fusion energy would, on the other hand, provide a consistent and clean source of energy, with a much smaller geographic footprint than existing renewables and no risk from long-lived radioactive waste and potential meltdown scenarios, as arises with energy production from fission. However, it is often joked that fusion is perpetually ‘20 years away’, which stems from its complex scientific, engineering and economic challenges. Artificial intelligence is proving a useful tool in allowing the field to learn faster. In this blog we outline the science behind fusion, why it is so important, and its challenges. We then highlight how AI is accelerating our progression to fusion energy, backed up with some relevant case studies.

What is Nuclear Fusion?

At the core of fusion lies the chemical reactions which power the stars, such as our sun. A flagship example is deuterium-tritium (D-T) fusion. Deuterium occurs naturally in sea water at about 30 grams per cubic metre. This makes it extremely abundant relative to our dwindling bank of fossil fuels. Tritium is rare in nature and radioactive with a half life of roughly 12 years. However, it can be bred inside the reactor itself using blankets containing lithium, which is found in very large quantities within the Earth’s crust, plus small amounts in seawater.

Figure 1: schematic of deuterium-fusion, which requires temperatures in excess of 100 million degrees. Image credit: https://www.sciencelearn.org.nz/ 

The reaction is clean and carbon-free and produces vast amounts of energy - the energy produced by 1 gram of D-T fuel is equivalent to the energy generated by burning 2,400 gallons of oil. It is clear to see that fusion offers a high-energy, clean and sustainable alternative to fossil fuels for meeting the energy needs of our growing population.

The Challenges of Fusion

If fusion is so compelling, why isn’t it powering the world already? At its core lies difficulties which push the boundaries of our current engineering capabilities. The fusion of two hydrogen atoms requires immense energy input to overcome repulsion resulting from the fact they both have positive electrical charges. This process therefore requires extreme pressures and temperatures, in excess of 100 million Kelvins. These conditions would instantly melt any known material, so we look toward magnetic confinement as a potential solution - this involves holding the charged plasma in place with a strong magnetic field to prevent contact with the reactor walls. Plasma turbulence, caused by temperature gradients in the plasma, is a major issue for fusion which can greatly damage the walls if contact is made. 

We need to find ways of predicting and controlling this plasma turbulence, as well as navigating layers of physics involving materials under intense neutron bombardment, and complex heat and particle transport. Against this backdrop, artificial intelligence is already making a tangible difference. From accelerating simulations that once took months to helping interpret experimental data in real time, AI tools are becoming embedded in how scientists design experiments, understand plasma behaviour, and optimise reactor concepts.

Case Studies of AI’s Success in Fusion

Google DeepMind and Commonwealth Fusion Systems: AI for real-time reactor control

Commonwealth Fusion Systems (CFS) has partnered with Google DeepMind to accelerate learning for the SPARC program. Central to this collaboration is TORAX, DeepMind’s open-source, differentiable tokamak core transport simulator, which enables rapid plasma calculations and integrates AI tools into a single optimisation framework [3]. The goal is to improve and optimise SPARC’s operation, ultimately supporting a more efficient and economical fusion plant. A major focus of the collaboration is reinforcement learning, a branch of machine learning in which AI systems learn through trial and error, refining their strategies based on feedback. By exploring vast operational parameter spaces, DeepMind’s tools can identify promising strategies for running SPARC more effectively [4]. 

Figure 2: visualisation of SPARC.

Image credit: Commonwealth Fusion Systems https://cfs.energy/technology/sparc 

The potential extends to real-time tokamak control. By training on a wide range of plasma scenarios, the AI could learn how to adjust the machine’s magnetic field configurations and plasma shaping parameters in pursuit of an objective, like maximising fusion power while remaining safely within operational limits. In collaboration with the Swiss Plasma Center at EPFL, DeepMind successfully used reinforcement learning to control the Tokamak à Configuration Variable (TCV), exploring complex plasma shapes in real time. By optimising plasma shape, AI systems could steer heat loads to protect components while maximising performance, adapting dynamically as new experimental data becomes available. Together, these advances suggest a future where AI does more than assist operators; it actively optimises fusion performance in real time, bringing practical fusion energy closer to reality.

MIT AI-Enhanced Plasma Simulations: AI for performance validation and optimisation

AI-enhanced plasma simulations at MIT’s Plasma Science and Fusion Center are helping to decode plasma behaviour and turbulence [5] [6]. The paper highlights how high-resolution turbulence simulations were used to confirm the expected performance of ITER, currently under construction in Southern France. In order to verify the baseline scenario - the plasma setup designed to achieve Q = 10 at a fusion power of 500MW - the authors used CGYRO, a code developed by General Atomics to run detailed simulations on the plasma behaviour within the device, using its initial operating conditions as inputs.

Figure 3: ITER under construction.

Image credit: ITER https://www.iter.org/iter-image-galleries/construction 

This hits on a key bottleneck in fusion - the computationally expensive nature of high-fidelity simulations. This is particularly prevalent in complex plasma modelling, such as turbulence. Surrogate modelling is an incredibly useful tool to speed up these analyses; they are simplified approximations of higher-order models, which are used to map input data to outputs quickly, without the need to perform lots of expensive simulations to evaluate the results of different configurations.

The simulation outputs from CGYRO were passed through the PORTALS framework, a collection of tools originally developed at MIT by Rodriguez-Fernandez. The framework uses machine learning to build a surrogate model which can be used to predict the results of the more complex runs quickly. In order to fully train the surrogate, its results were checked against the complex runs and refined repeatedly until it accurately predicted the results from CGYRO. Although some effort is required to train these models, in the long run they can be used to predict the results of simulations across a vast design space quickly and accurately, without running new simulations. The authors were able to use the surrogate models to confirm the baseline scenario configuration, and were even able to explore alternate configurations, leading to the discovery that a different setup could produce the same energy with less energy input.

Research at PPPL: AI for diagnostics and aiding theoretical research

Princeton Plasma Physics Laboratory (PPPL) has developed an approach using AI to generate synthetic diagnostic signals from other sensors in the system to reconstruct missing data streams, for instance, if one sensor fails. This AI, known as Diag2Diag [7], represents a significant step toward more robust control, including improved reactor resilience. It also assures the ability of the reactor to work 24/7 without interruption, which is essential for reliable power generation.

Diag2Diag solves certain problems with plasma measurements, including the monitoring of sudden instabilities, which are often too quick to be detected and make reliable power production difficult. An example of this lies with the Thomson scattering diagnostic, used in tokamaks. This diagnostic measures the temperature, density and number of electrons within the plasma - where often measurements are not taken quickly enough to assure the stability of the plasma. Diag2Diag allows measurements to be taken where the Thomson scattering diagnostic is limited. In this case, it demonstrates the support of the magnetic island theory for the suppression of ELMs (edge-localised modes), which are large bursts of energy that cause severe damage to reactor first walls. Understanding this mechanism is crucial for the development of commercial fusion reactors. In addition, by reducing the number of diagnostic systems needed, the system is made much more compact; this will ultimately result in a reduction of the cost and complexity of the machine.

Figure 4: images of an edge-localised mode. (a) the start of an ELM and (b) during the eruption. Images captured in the spherical MAST tokamak.

Image credit: https://doi.org/10.1063/1.2939030 

Many interesting studies are being carried out in this area at PPPL, including research into machine learning for avoiding tearing instabilities, tokamak control systems and improved stellarator designs [8].

nTtau Digital: AI for accelerated modelling and optimisation of reactor designs

AI’s impact certainly isn’t restricted to the plasma,  it also lies within how fast you can explore plant-level design trade-offs and their impact on cost and performance. Our automated design platform, Nuplant^TM [9], integrates multi-objective optimisation with surrogate models trained on high-fidelity physics into an automated workflow to drastically cut design times and enable quick exploration of the entire design space, from the fusion core to the site boundary.

Our platform begins with a parametric CAD geometry, closely followed by simulation workflows which include meshing, high-fidelity physics analysis, multi-objective optimisation and costing, all wrapped-up within an automated pipeline. Our workflow allows not just one, but thousands of design configurations to be explored - ultimately resulting in three configurations optimised for engineering design, physics, and cost respectively, along with detailed, traceable costing reports which are generated using codes developed by Woodruff Scientific [10] [11]. 

Figure 5: example results from multi-objective optimisation, showing stellarator designs optimised for cost (left), manufacturability (centre) and physics (right).

Image credit: nTtau Digital.

Even with sophisticated AI tools, many challenges remain before fusion has a chance at being commercially viable. However, it has thus-far proven to alleviate some significant bottlenecks, enabling faster design timelines, real-time reliability and economic viability. AI won’t solve fusion on its own, and caution is necessary to ensure reliability and transparency, with black-box conclusions consciously avoided. However, we are clearly seeing a reshaping of our toolkit, which may be pivotal in deciding how quickly fusion moves from the lab to the grid.