FUSION | REVEALING UNCERTAINTY
hundreds of simulations, they can run tens and reach the same level of understanding,” Niedfeldt says. “That saves a significant amount of computational time and resource.” Another application involves diagnostic and sensor
placement for the STEP fusion reactor programme. In extreme environments where sensors are difficult and expensive to replace, placement decisions made during design have long-term consequences. As Niedfeldt observes: “Once they’re installed, it is difficult, expensive or sometimes impossible to move, replace, or repair them.” She explains how the digiLab tool helps: “We look at where to place sensors to get the maximum information from the machine during operation”. Fusion has emerged as an early adopter of advanced
Above: University of Exeter spin out company digiLab, based at Exeter Quay, focuses on uncertainty quantification and probabilistic machine learning. Source: digiLab
offer a pathway toward regulatory acceptance. They align more naturally with existing safety case structures, which already rely on quantified margins and confidence arguments.
Fusion as an AI testbed The company’s most extensive nuclear engagement to date has been with the UK Atomic Energy Authority (UKAEA) and with the newly formed organisation to deliver the UK’s first fusion plant: UK Industrial Fusion Solutions (UKIFS). The collaboration began around four years ago, with digiLab initially acting as an uncertainty quantification partner. “We were brought in specifically for uncertainty quantification,” Niedfeldt explains, adding: “That role has grown alongside UKAEA’s Compute Division, as they look to adopt machine learning in a controlled and scalable way.” Rather than isolated pilot studies, UKAEA is deploying
Below: The STEP Fusion reactor will be built at the site of a former coal-fired power station. Source: STEP Fusion
digiLab’s platform on-premises to enable its wider use among its engineers and scientists. This reflects a shift from experimental application to institutional capability building. One major programme has focused on plasma physics simulations, which are computationally intensive and can take days or weeks to run. In this case digiLab tools are used to determine where additional simulations will provide the most information. “Instead of running
digital and AI-driven methods, in part because it lacks the legacy systems and regulatory constraints found in the nuclear fission sector. Nonetheless, Niedfeldt sees this as an opportunity for cross-sector learning: “Fusion has to adopt these technologies. They’re dealing with problems humans can’t easily hold in their heads, and many things are still not well understood.” She continues: “There’s a real opportunity for intentional transfer of technology from fusion into fission. What’s learned about diagnostics, uncertainty, and model integration can feed back into more established nuclear applications.” The same methodologies are thus applicable beyond fusion, including in fission reactors, SMRs, and other complex industrial systems. Looking ahead, Niedfeldt anticipates that probabilistic machine learning will become a standard part of engineering education and practice. “In the future, every engineer and research scientist will use these methods,” she predicts, comparing their adoption to that of tools such as MATLAB in previous decades. However, scaling adoption does present challenges.
For example, many engineers currently in industry were not trained in machine learning or probabilistic methods, and wholesale retraining is unrealistic. “We don’t think everyone needs to become a
coder,” Niedfeldt says. “The challenge is making these technologies accessible through workflows that fit existing engineering practice.” This philosophy underpins digiLab’s platform design, which supports both scripting-based and visual, drag-and-drop interfaces. The objective is to lower barriers to entry while maintaining methodological rigour.
Building trust While nuclear remains a core focus, digiLab is expanding into other sectors with similar characteristics, including aerospace, national infrastructure, and environmental monitoring. But despite this breadth, digiLab emphasises that its mission remains centred on safety, reliability, and responsible use of AI. “There are many ways AI can be misused,” Niedfeldt
says. “But in engineering and scientific domains, it has enormous potential to help us understand systems better, reduce risk, and make safer decisions.” By grounding machine learning in probabilistic reasoning, physical understanding, and explicit uncertainty, digiLab is attempting to bridge the gap between emerging digital capability and the nuclear industry’s long- standing demand for trust. ■
22 | February 2026 |
www.neimagazine.com
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