  Considerable research has been devoted to deep learning-based predictive
models for system prognostics and health management in the reliability and
safety community. However, there is limited study on the utilization of deep
learning for system reliability assessment. This paper aims to bridge this gap
and explore this new interface between deep learning and system reliability
assessment by exploiting the recent advances of physics-informed deep learning.
Particularly, we present an approach to frame system reliability assessment in
the context of physics-informed deep learning and discuss the potential value
of physics-informed generative adversarial networks for the uncertainty
quantification and measurement data incorporation in system reliability
assessment. The proposed approach is demonstrated by three numerical examples
involving a dual-processor computing system. The results indicate the potential
value of physics-informed deep learning to alleviate computational challenges
and combine measurement data and mathematical models for system reliability
assessment.
