An open computational framework for reliability based optimization

E. Patelli; M. De Angelis

An open computational framework for reliability based optimization

E. Patelli, M. De Angelis

Civil And Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

Abstract

This paper presents an open computational framework for reliability based. The framework has been designed to provide the maximum flexibility allowing the state of the art in reliability analysis (e.g. adopting advanced Monte Carlo methods) to be combined in the direct approach as well as in the construction of the different type of meta-models (e.g. response surface, artificial neural networks, kriging model and polynomial chaos, etc.). A set of widely used gradient-based and gradient-free optimization algorithms are also available for performing the optimization step as well as high performance computing capability. Numerical applications show the applicability and flexibility of the proposed framework for solving real-life problems.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Computational Structures Technology, CST 2012
Publisher	Civil-Comp Press
Number of pages	16
Volume	99
ISBN (Print)	9781905088546
Publication status	Published - 7 Sep 2012
Event	11th International Conference on Computational Structures Technology, CST 2012 - Dubrovnik, Croatia Duration: 4 Sep 2012 → 7 Sep 2012

Conference

Conference	11th International Conference on Computational Structures Technology, CST 2012
Country	Croatia
City	Dubrovnik
Period	4/09/12 → 7/09/12

Keywords

high performing computing
Matlab
meta modelling
numerical methods
open source
reliability based optimization

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AB - This paper presents an open computational framework for reliability based. The framework has been designed to provide the maximum flexibility allowing the state of the art in reliability analysis (e.g. adopting advanced Monte Carlo methods) to be combined in the direct approach as well as in the construction of the different type of meta-models (e.g. response surface, artificial neural networks, kriging model and polynomial chaos, etc.). A set of widely used gradient-based and gradient-free optimization algorithms are also available for performing the optimization step as well as high performance computing capability. Numerical applications show the applicability and flexibility of the proposed framework for solving real-life problems.

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