Assessing decision boundaries under uncertainty

Aquino, Wilkins; Desmond, Jacob Richard; Eldred, Michael S.; Kurzawski, Andrew Joseph; McCormick, Cameron; Sanders, Clay Michael; Smith, Chandler Baldwin; Walsh, Timothy

doi:10.1007/s00158-024-03832-9

Assessing decision boundaries under uncertainty

Journal Article · Sat Jun 29 00:00:00 EDT 2024 · Structural and Multidisciplinary Optimization

DOI:https://doi.org/10.1007/s00158-024-03832-9· OSTI ID:2406710

Aquino, Wilkins ^[1]; Desmond, Jacob Richard ^[2]; Eldred, Michael S. ^[2]; Kurzawski, Andrew Joseph ^[2]; McCormick, Cameron ^[2]; Sanders, Clay Michael ^[2]; Smith, Chandler Baldwin ^[2]; Walsh, Timothy ^[2]

Duke University, Durham, NC (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

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 paper 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 approach proposed in this paper is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2406710

Report Number(s):: SAND--2024-09484J

Journal Information:: Structural and Multidisciplinary Optimization, Journal Name: Structural and Multidisciplinary Optimization Journal Issue: 7 Vol. 67; ISSN 1615-147X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
Adaptive Machine Learning
Decision Boundaries
Support Vector Machines
Uncertainty Quantification

Assessing decision boundaries under uncertainty

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