Bruno Sudret

Professor, Chair of Risk, Safety and Uncertainty Quantification, ETH Zürich, Switzerland

Active learning methods for component and system reliability analysis 

Structural reliability analysis evaluates the safety of structures and systems under uncertain conditions. Traditional simulation methods for estimating failure probability are computationally intensive. As an alternative, the past decade has seen the introduction of surrogate-based methods. These methods involve creating a metamodel of the original limit-state functions for use in reliability estimation algorithms. Active learning techniques iteratively refine the surrogate model's experimental design using suitable learning functions.

This presentation surveys recent advancements inactive learning for reliability analysis, identifying a common framework that includes a surrogate model, a reliability estimation algorithm, a learning function, and a stopping criterion. By non-intrusively integrating these components, we can reconstruct most existing active learning methods. We conducted a comprehensive benchmark of 39 active learning strategies across 20 selected reliability problems, resulting in approximately 12,000 analyses. This benchmarking helped identify performance patterns and generalization capabilities of different approaches, leading to best practice recommendations.

The second part of the talk extends these methods to system reliability analysis, where surrogate models are developed for each limit state separately. We introduce an optimal enrichment scheme, informed by global sensitivity analysis, to prioritize surrogate model updates. The talk concludes with various structural engineering applications, showcasing the practical applications of these methods.

Personal Profile:
Bruno Sudret is a professor of Risk, Safety and Uncertainty quantification at ETH Zurich since 2012. His teaching and research interests are computational methods for uncertainty quantification, reliability and sensitivity analysis, Bayesian approaches for model calibration and reliability-based design optimization, among others.

B. Sudret received a master’s of science from the Ecole Polytechnique (France) in 1993. He then obtained a master’s degree and a Ph.D in civil engineering from the “Ecole Nationale des Ponts et Chaussees” (France) in 1996 and 1999, respectively. Dr. Sudret has been working in probabilistic engineering mechanics and uncertainty quantification for engineering systems since 2000: first as a post-doctoral fellow at the University of Berkeley (California), then as a researcher at EDF R&D (the French world leader in nuclear power generation) where he was the head of a group specialized in probabilistic engineering mechanics (2001-2008). From 2008 to 2011 he has worked as the Director of Research and Strategy at Phimeca Engineering (France).

B. Sudret is the author and co-author of more than 300 publications in journal and conference proceedings. He currently serves in the editorial board of Reliability Engineering and Systems Safety, Probabilistic Engineering Mechanics and Structural Safety. He promotes the dissemination of uncertainty quantification techniques through the development of the software UQLab ( and the community platform UQWorld (