Surrogate-Model Accelerated Random Search algorithm for global optimization with applications to inverse material identification

An optimization algorithm is proposed which is applicable for the global optimization of computationally expensive functions with specific applications in material identification. The methodology, referred to as the Surrogate-Model Accelerated Random Search (SMARS) algorithm, is a non-gradient based...

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Bibliographic Details
Published in:	Computer methods in applied mechanics and engineering Vol. 196; no. 45; pp. 4561 - 4576
Main Authors:	Brigham, John C., Aquino, Wilkins
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 15.09.2007 Elsevier
Subjects:	Computational techniques Exact sciences and technology Finite elements Fundamental areas of phenomenology (including applications) Inverse problems Mathematical methods in physics Measurement and testing methods Optimization Physics Random search Solid mechanics Static elasticity (thermoelasticity...) Structural and continuum mechanics Surrogate-model Viscoelasticity Surrogate-model Viscoelasticity Finite elements Inverse problems Optimization Random search Thermal diffusion Local search Probabilistic approach Rheology Optimization method Iterative method Global optimum Neural network Modeling Search algorithm Inverse problem Functionnally graded material Material testing System identification
ISSN:	0045-7825, 1879-2138
Online Access:	Get full text
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