AI nuclear fusion: Sustainable power generation meets powerhouse computing

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  Quantumrun Foresight
• July 18, 2022

Insight summary

Nuclear fusion, a potential source of abundant and clean energy, has seen significant advancements through artificial intelligence (AI) applications in plasma analysis and predictive modeling. These AI-driven innovations are accelerating the fusion research process, making it more efficient and reducing risks and costs associated with equipment damage. The broader societal impact may include a shift in energy production methods, increased focus on STEM education, and potential geopolitical changes as fusion energy becomes more viable.

AI nuclear fusion context

Scientists have been striving to develop a stable, safe, and continuously energy-generating nuclear fusion process since the 1940s. This process, once perfected, promises to offer an economical, environmentally friendly, and virtually limitless source of power. It has the potential to significantly reduce reliance on traditional electricity sources, such as fossil fuels and to a certain extent, renewable energy sources. 

In 2021, Swedish computer scientists Stefano Markidis and Xavier Aguilar made a significant contribution to this field. They developed a deep learning AI algorithm that effectively simplifies a complex step in analyzing plasma, a key component in nuclear fusion. This step involves calculating the electromagnetic field of plasma. Their method proved to be quicker and more efficient than traditional approaches, which relied on intricate mathematical formulas. 

Further demonstrating the potential of AI in nuclear fusion research, Kyle Morgan and Chris Hansen from the University of Washington introduced a novel technique. Their research, focusing on predicting plasma behavior, utilizes machine learning (ML), specifically a statistical method known as regression. This approach effectively filters out scenarios leading to illogical results. As a result, their system operates with fewer data, reduced processing resources, and less time. 

Disruptive impact

The integration of AI in nuclear fusion research is poised to transform how scientists manage plasma volatility in fusion tests. Plasma instability is a critical challenge; when plasma becomes volatile, it can breach containment and damage or even destroy expensive equipment. Utilizing AI models to predict such disturbances equips scientists with crucial foresight. Accurate predictions of plasma behavior allow for timely adjustments, reducing the risk of costly equipment failures and experiment disruptions.

AI's application also serves as a powerful tool in analyzing data from failed experiments. By examining these failures, AI can uncover patterns and insights that might elude human researchers. This analysis can lead to the development of innovative engineering solutions, enhancing the overall efficiency and safety of fusion experiments. As scientists gain a deeper understanding of disruption causes, they can devise strategies to make these occurrences less frequent. This continuous learning cycle, powered by AI, is essential in refining the fusion process, ultimately contributing to a more stable and reliable energy source.

Furthermore, AI's ability to solve complex mathematical equations related to plasma research is crucial. These equations are integral to understanding plasma behavior but are often time-consuming to solve manually. AI accelerates this process, providing faster and more accurate results. This acceleration is vital for the advancement of nuclear fusion research, moving it closer to commercial viability.

Implications of applying AI to nuclear fusion research

Wider implications of AI systems being applied to nuclear fusion research may include:

• AI-driven iterative design processes in fusion energy development, leading to optimized plant designs and efficient resource utilization through digital twin simulations.


• (2040s) eco-friendly businesses increasingly adopting nuclear fusion as a sustainable alternative to conventional electricity sources, reducing their carbon footprint.


• (2040s) The gradual reduction of the workforce in traditional fossil fuel power plants, as nuclear fusion becomes more accessible to the public.


• Governments enacting policies to manage the transition from fossil fuels to fusion energy, ensuring a balanced and equitable shift in the energy sector.


• Increased investment in STEM education and training programs, preparing the future workforce for emerging jobs in the nuclear fusion industry.


• The emergence of new business models in the energy sector, focusing on decentralized and community-based fusion power generation.


• Enhanced global energy security as countries become less dependent on imported fossil fuels and more reliant on domestically produced fusion energy.


• Potential geopolitical shifts as countries with advanced nuclear fusion technology gain influence in the global energy market.

Questions to consider

• Do you think renewables like solar, wind and next-gen batteries will make fusion energy redundant by the time fusion tech is perfected and made commercially viable?


• How is AI being applied to enhance the engineering of other forms of energy production?