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Assessing decision boundaries under uncertainty

Structural and Multidisciplinary Optimization

Desmond, Jacob R.; Walsh, Timothy; Mccormick, Cameron; Smith, Chandler B.; Kurzawski, Andrew J.; Sanders, Clay M.; Eldred, Michael S.; Aquino, Wilkins

In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this works offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The proposed approach is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.

More Details
TYPE Journal Article YEAR 2024
DOIOSTIScopus
Results 1501–1525 of 101,000
Results 1501–1525 of 101,000
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