Trusted Deep Learning For Multi-Domain Engineering Systems. The efficient design and operation of engineering systems require trusted predictive model

Trusted Deep Learning For Multi-Domain Engineering Systems. The efficient design and operation of engineering systems require trusted predictive models using knowledge sources from different physical domains. These domains (e..g. thermal, vibration, electrical, etc.) are traditionally siloed specialisms within industry with their own methods and language. This project addresses the challenges in producing unifying models that will be used to better holistically understand engineering system behaviour and health, while accommodating the realities of imperfect training data sets. The project will build upon the state-of-the-art in machine learning systems to create interconnected modular models that can be interpreted by experts (i.e. that are explicable) capturing both known physics and complex (e.g. emergent from domain coupling) behaviours captured only in real system data.