Reliability analysis is essential for the development, design and assessment of engineering systems under uncertainty. Challenges in computing the probability of failure are associated with non-linear system behaviour, large numbers of uncertain parameters and failure/rare events inducing multiple, disconnected failure domains. We invite talks discussing efficient computational methods for simulating rare events and quantifying failure probabilities based on sampling, surrogate modelling and machine learning as well as approximation approaches. Relevant applications of these techniques are in the assessment of static and dynamical engineering systems, reliability-based design optimization, reliability-oriented sensitivity analysis, Bayesian updating of failure probabilities with real-time data and applications in digital twin models.