Journal ArticleDOI
Computational methods in optimization considering uncertainties – An overview
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TLDR
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.About:
This article is published in Computer Methods in Applied Mechanics and Engineering.The article was published on 2008-11-15. It has received 487 citations till now. The article focuses on the topics: Probabilistic-based design optimization & Uncertainty quantification.read more
Citations
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Bayesian system identification based on probability logic
TL;DR: This application of Bayes' Theorem automatically applies a quantitative Ockham's razor that penalizes the data‐fit of more complex model classes that extract more information from the data.
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Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.
Journal ArticleDOI
Nonlinear system identification in structural dynamics: 10 more years of progress
TL;DR: In this paper, the authors present a survey of the key developments which arose in the field since 2006, and illustrate state-of-the-art techniques using a real-world satellite structure.
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Robust topology optimization accounting for spatially varying manufacturing errors
TL;DR: In this article, a robust approach for the design of macro-, micro-, or nano-structures by means of topology optimization, accounting for spatially varying manufacturing errors is presented.
Journal ArticleDOI
A Chebyshev interval method for nonlinear dynamic systems under uncertainty
TL;DR: In this article, a Chebyshev polynomial series-based interval analysis method for nonlinear systems with uncertain-but-bounded parameters is proposed. But the method is only suitable for problems with small uncertain levels.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Related Papers (5)
Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design
Xiaoping Du,Wei Chen +1 more