3 Jul 2024
Feasibility of Digital Twins from Multiple Data Sources for Improved Decision-Making
This project aims to establish the feasibility of integrating data from multiple sensory sources and modelling simulations into a digital twin of aircraft and physical infrastructure, enhanced by machine learning, in support of RAF operations and real-time decision-making. The disparate data will be amalgamated into a data repository, which will exchange data with the digital twin during operations.
Funded by The Royal Air Force
Principal investigators: Ashraf El-Hamalawi (Lead) and Peter Demian
Duration: 01/01/2024 – 30/06/2025
