Wind turbines, hydrogen plants, and road traffic will get digital twins that update themselves in real time, without needing to be taken offline for retraining. This project fuses physics-based modelling—reliable but computationally expensive—with machine learning, which is fast but struggles with generalisation. By combining the two into “scientific machine learning,” the researchers aim to create digital counterparts of physical systems that can simulate, predict, and optimise behaviour on the fly, using data from UK facilities and experiments. The core problem is that most scientific machine learning models must be re-trained offline when new data arrives, creating a bottleneck for real-time decisions. This project transforms that offline paradigm into a continuous, adaptive approach, minimising energy use and emissions by making training more efficient. If successful, the digital twins could solve currently intractable problems in wind energy, hydrogen, and road transportation—enabling faster grid balancing, safer hydrogen infrastructure, and smoother traffic flow. The technical achievements will also be transferred to policy-making, helping regulators test scenarios without disrupting real systems.
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In this project, we will seamlessly combine two disciplines that have been historically received continuous government and industrial funding: physics-based modelling, which is generalisable and robust but may require tremendous computational cost, and machine learning, which is adaptive and fast to be evaluated but not easily generalisable and robust. The intersection of the two spawns scientific machine learning, which maximises the strengths and minimises the weaknesses of the two approaches. The data will be provided by high-fidelity simulations and experiments, from the UK state-of-the-art facilities and software. The efficiency of the machine learning training will be maximised for the algorithms to require minimal energy (thereby, producing minimal emissions by minimising electricity consumption). This project builds upon large UK and EU funded expertise in scientific machine learning and simulation, which will be generalised to fast, real-time decision making. The most significant bottleneck of most scientific machine learning is that they need time to be re-trained offline when new data becomes available. We will transform offline paradigms into real-time approaches for the models to re-adapt and provide accurate estimates on the fly. This project will culminate into the delivery of practical digital twins (defined as digital counterparts of real world physical systems or processes that can be used for simulation, prediction of behaviour to inputs, monitoring, maintenance, planning and optimisation) to solve currently intractable problems in wind energy, hydrogen, and road transportation. This project will transfer the technical achievements and real-time digital twin to policy-making.
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