How do we know when something is about to go wrong?
Machine learning is opening new possibilities for monitoring Small Modular Reactors using neutron noise The transition away from fossil fuels is one of the main challenges of this century. Electricity grids that have long depended on coal and natural gas need to be significantly decarbonised within a timeframe measured in decades, not generations. Renewable sources such as wind, solar, and hydropower are expanding rapidly, but their variable and location-dependent output means that alternative low-carbon generation will remain essential for grid stability at various locations. Nuclear power is the most established technology capable of filling that role on a large scale, and it is getting attention across many national energy policies as a result. Beyond electricity generation, nuclear reactors – and Small Modular Reactors (SMRs) in particular – also offer high potential for process heat, district heating, and hydrogen production, opening pathways to decarbonise heavy industries such as steel and petrochemicals. Within this renewed interest, a particular category of reactor design has emerged as a very attractive option: SMRs. SMRs are defined as reactors with an …








