Gerhart I. Schuëller

TL;DR: In this article, the authors present a brief survey on some of the most relevant developments in the field of optimization under uncertainty, including reliability-based optimization, robust design optimization and model updating.

TL;DR: A critical appraisal of reliability procedures for high dimensions is presented and it is observed that some types of Monte Carlo based simulation procedures in fact are capable of treating high dimensional problems.

TL;DR: In this paper, the authors present an alternative method to calculate failure probabilities which combines the advantages of both the importance sampling technique (accuracy and the possibility of error estimation) and the design point calculation (identification of the region where highest failure probability is to be expected).

TL;DR: This contribution provides a survey on approaches for performing Reliability-based Optimization, with emphasis on the theoretical foundations and the main assumptions involved.

TL;DR: A sampling technique which uses lines in order to probe the failure domain, which is employed in conjunction with a stepwise procedure which makes use of Markov Chains and exhibits accelerated convergence.