Model calibration, optimal design, and data-assimilation for digital twins of fusion reactor components

This PhD project aims to enhance the digital twinning process for fusion energy components by leveraging physics-based modelling, data analysis, and artificial intelligence (AI) techniques.

The primary objective is to accelerate the digital twinning loop, which involves continuously refining and improving digital twin models based on real-world data and feedback.

In addition to reviewing physics-informed neural networks and standard finite element modelling, the project will also investigate the possibility of improving finite element-based analysis by embedding some modern AI principles. This could involve manipulating the FE shape function using neural networks to conserve the formulation's energy.

The PhD project ensures that all applications investigated are directly relevant to the challenges faced by the UK Atomic Energy Authority (UKAEA) in fusion energy research.

This involves addressing issues related to component design, performance optimization, safety considerations, and other relevant aspects within fusion energy systems.