Reliability analysis offers the possibility of quantifying the level of safety of engineering systems. As reliability analysis encompasses probability theory, it is well suited for problems where uncertainty is of the aleatoric type, that is, arising from inherent randomness. However, in several practical situations, uncertainty may be of the epistemic type due to issues such as lack of knowledge, conflicting sources of information, vagueness, etc. In such case, non-traditional models such as intervals or fuzzy sets may be a suitable choice for describing uncertainty. Naturally, in practice, one may be confronted with the challenge of coping with both aleatoric and epistemic uncertainty, leading to a problem of imprecise reliability analysis.

Imprecise reliability analysis offers a powerful framework for coping with uncertainties. In essence, it provides a collection of reliability analyses (performed under aleatory uncertainty) which are indexed by the model describing epistemic uncertainty. Nonetheless, its practical implementation is far from trivial, as it demands increased numerical efforts when compared with purely aleatoric reliability analysis due to the necessity propagating aleatoric and epistemic uncertainty simultaneously. Therefore, the aim of this mini-symposium is addressing the very latest development on approaches for reliability under aleatoric and epistemic uncertainty. The scope of the mini-symposium is broad, as it covers: different models for representing uncertainty such as classical probabilities, intervals, fuzzy analysis, imprecise probabilities, evidence theory, etc.; novel formulations for coping with aleatoric and epistemic uncertainty; advanced simulation methods; development and application of surrogate models, etc. Both theoretical developments and applications involving systems of engineering interest are particularly welcomed in this session.

This activity is organized under auspices of the Committee on Probability and Statistics in Physical Sciences (C(PS)^2) of the Bernoulli Society for Mathematical Statistics and Probability.