Digital twins are dynamic and self-evolving models that simulate a physical asset and represent its exact state through bi-directional data assimilation. They employ Artificial Intelligence (AI) data-driven and symbolic techniques to provide state synchronization, monitoring, control and decision support.
This project will build on the results of the ongoing ESA-funded project ExploDTwin (“”Digital Twin for Space Exploration Assets””), which aims at integrating the DT into the ESA infrastructure to support online space assets operations with functionalities such as planning, what-if-analysis, fault detection, diagnosis and prognosis. To this end, ExploDT introduces a model-based design methodology that allows to seamlessly integrate engineering methods and AI techniques into a cohesive DT framework.
The PhD student will investigate new formal methods to analyze the DTs by integrating model checking, automated theorem proving, simulation, and machine learning. Different aspects of the DTs will be considered including temporal properties for validation, monitorability and diagnosability. The new methods will be implemented and evaluated on space related benchmarks derived from ExploDTwin results.
