Design of complex systems in the presence of large uncertainties: A statistical approach

P. S. Koutsourelakis; Phadeon-Stelios Koutsourelakis

doi:10.1016/j.cma.2008.04.012

Design of complex systems in the presence of large uncertainties: A statistical approach

P. S. Koutsourelakis, Phadeon-Stelios Koutsourelakis

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

Abstract

The present paper proposes an algorithmic framework for designing complex systems in the presence of large uncertainties. It is highly applicable to realistic engineering problems as it is directly parallelizable and can interact in a non-intrusive manner with any deterministic solver (e.g. finite element codes) in order to quantify response statistics and their dependence on design variables. The efficiency of the proposed procedure, which in complex systems is equivalent to the number of calls to the deterministic solver, is high due to the optimized sampling process which is employed in conjunction with a Bayesian, statistical learning component. Several numerical examples, dealing with static and dynamic, linear and nonlinear systems demonstrate the accuracy and effectiveness of the proposed methodology. In addition robust sensitivity measures for systems in the presence of uncertainties are provided. (c) 2008 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	4092-4103
Number of pages	12
Journal	Computer Methods in Applied Mechanics and Engineering
Volume	197
Issue number	49-50
DOIs	https://doi.org/10.1016/j.cma.2008.04.012
Publication status	Published - 2008

Keywords

Uncertainty
Complex systems
Statistical learning
Monte Carlo
Bayesian
Design
MONTE-CARLO
SIMULATION
MODELS

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10.1016/j.cma.2008.04.012

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title = "Design of complex systems in the presence of large uncertainties: A statistical approach",

abstract = "The present paper proposes an algorithmic framework for designing complex systems in the presence of large uncertainties. It is highly applicable to realistic engineering problems as it is directly parallelizable and can interact in a non-intrusive manner with any deterministic solver (e.g. finite element codes) in order to quantify response statistics and their dependence on design variables. The efficiency of the proposed procedure, which in complex systems is equivalent to the number of calls to the deterministic solver, is high due to the optimized sampling process which is employed in conjunction with a Bayesian, statistical learning component. Several numerical examples, dealing with static and dynamic, linear and nonlinear systems demonstrate the accuracy and effectiveness of the proposed methodology. In addition robust sensitivity measures for systems in the presence of uncertainties are provided. (c) 2008 Elsevier B.V. All rights reserved.",

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T1 - Design of complex systems in the presence of large uncertainties: A statistical approach

AU - Koutsourelakis, P. S.

AU - Koutsourelakis, Phadeon-Stelios

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N2 - The present paper proposes an algorithmic framework for designing complex systems in the presence of large uncertainties. It is highly applicable to realistic engineering problems as it is directly parallelizable and can interact in a non-intrusive manner with any deterministic solver (e.g. finite element codes) in order to quantify response statistics and their dependence on design variables. The efficiency of the proposed procedure, which in complex systems is equivalent to the number of calls to the deterministic solver, is high due to the optimized sampling process which is employed in conjunction with a Bayesian, statistical learning component. Several numerical examples, dealing with static and dynamic, linear and nonlinear systems demonstrate the accuracy and effectiveness of the proposed methodology. In addition robust sensitivity measures for systems in the presence of uncertainties are provided. (c) 2008 Elsevier B.V. All rights reserved.

AB - The present paper proposes an algorithmic framework for designing complex systems in the presence of large uncertainties. It is highly applicable to realistic engineering problems as it is directly parallelizable and can interact in a non-intrusive manner with any deterministic solver (e.g. finite element codes) in order to quantify response statistics and their dependence on design variables. The efficiency of the proposed procedure, which in complex systems is equivalent to the number of calls to the deterministic solver, is high due to the optimized sampling process which is employed in conjunction with a Bayesian, statistical learning component. Several numerical examples, dealing with static and dynamic, linear and nonlinear systems demonstrate the accuracy and effectiveness of the proposed methodology. In addition robust sensitivity measures for systems in the presence of uncertainties are provided. (c) 2008 Elsevier B.V. All rights reserved.

KW - Uncertainty

KW - Complex systems

KW - Statistical learning

KW - Monte Carlo

KW - Bayesian

KW - Design

KW - MONTE-CARLO

KW - SIMULATION

KW - MODELS

U2 - 10.1016/j.cma.2008.04.012

DO - 10.1016/j.cma.2008.04.012

M3 - Article

SN - 1879-2138

VL - 197

SP - 4092

EP - 4103

JO - Computer Methods in Applied Mechanics and Engineering

JF - Computer Methods in Applied Mechanics and Engineering

IS - 49-50

ER -

Design of complex systems in the presence of large uncertainties: A statistical approach

Abstract

Keywords

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