The digital twin of fusion energy components

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Key objectives/Aims: (1) Developing a digital twin platform that replicates physical experiments: The project will explore the possibilities of building a digital twin for fusion energy applications. This includes purely data-driven digital twin and physics-based digital twin platforms. This part of the project consists of an intensive survey of available literature, training in AI, data collection, and computational mechanics. (2) Using the inverse methodology and AI to build a physics-informed digital twin: One of the significant challenges in building a digital twin is a platform that can replicate the reality with sparse data input. Therefore, this part will use inverse analysis using AI, physics informed neural networks, and computational mechanics to understand whether the data gap can be robustly and accurately filled. In addition, this step will investigate the possibility of establishing a problem in its entirety from sparse data. (3) Putting (1) and (3) together to verify the digital twin against test data: The lessons learnt in steps 1 and 2 will be employed to investigate how accurately component testing may be replicated. This step involves retrospective replication of experiments and forecasting the future of how the system/components behave. Novelty: Combining data, AI and computational mechanics to deliver a physics-based digital twin is a new concept. Establishing the problem in its entirety using inverse models, sparse data, AI, and computational mechanics is the physical sciences and engineering challenge.