Abstract Machine learning (ML) algorithms allow computers to learn without being explicitly
programmed. Their utilization is spreading to highly sophisticated tasks across multiple …

Engineering design decisions inherently are made under risk and uncertainty. The
characterization of this uncertainty is an essential step in the decision process. In this paper …

The robustness of a design has a major influence on how much the product's performance
will vary and is of great concern to design, quality, and production engineers. While …

This paper proposes formulations and algorithms for design optimization under both
aleatory (ie, natural or physical variability) and epistemic uncertainty (ie, imprecise …

This paper proposes formulations and algorithms for design optimization of multidisciplinary
systems under both aleatory uncertainty (ie, natural or physical variability) and epistemic …

This paper proposes three machine learning-based uncertainty set construction methods
and a novel uncertainty quantification method for robustness-based optimization. The …

The development of product families, groups of products that share a common platform, is
one way to provide product variety while keeping design and production costs low. The …

In systems engineering, verification activities evaluate the extent to which a system under
development satisfies its requirements. In large systems engineering projects, multiple firms …

An well-established tool for probabilistic analysis is the Monte Carlo simulation (MCS),
which can be used to gain insight into an unknown system behavior. The variability of the …

In this paper, we propose a new likelihood-based methodology to represent epistemic
uncertainty described by sparse point and/or interval data for input variables in uncertainty …