Optimization of Complex Systems in the Presence of Uncertainty and Approximations
Loading...
Authors
Smith, Kevin B.
Subjects
engineering risk
reliable design
optimization
regression
probability of failure
measures of risk
quantiles
superquantiles
reliable design
optimization
regression
probability of failure
measures of risk
quantiles
superquantiles
Advisors
Date of Issue
2014-08-24
Date
Publisher
Air Force Research Laboratory
Language
Abstract
Engineering decisions are invariably made under substantial uncertainty about current and future system cost and response, including cost and response associated with low-probability but high-consequence events. Such events motivate approaches that typically have centered on constraining or minimizing probability of failure, in contrast to the risk-neutral approach of constraining or minimizing expected values. The research under this proposal has, instead, developed concepts of risk-averse decision making between these extremes with the aim of achieving an advanced methodology better able to deal with risks and reliability in engineering design. Measures of risk that go beyond statistical quantiles to so-called superquantiles (CVaR) and their mixtures have been the main focus. The results have explored their superior properties and enhanced computability along with surprising implications that standard least-squares regression in statistical approximations might better be supplanted by generalizations like quantile and even superquantile regression. Superquantile regression, which provides a cautious and powerful tool, is completely new. It is entirely a product of this grant research.
Type
Report
Description
Series/Report No
Department
Organization
University of Washington
Identifiers
NPS Report Number
Sponsors
The research was a collaborative effort with Johannes Royset of the Naval Postgraduate School, who had separate funding from AFOSR.
Air Force Office of Science and Research
Air Force Office of Science and Research
Funder
Format
Citation
Distribution Statement
Distribution A - Approved for public release
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.