Shape Optimization under Uncertainty from a Stochastic Programming Point of View

Optimization problems are relevant in many areas of technical, industrial, and economic applications. At the same time, they pose challenging mathematical research problems in numerical analysis and optimization. Harald Held considers an elastic body subjected to uncertain internal and external forc...

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Wedi'i Gadw mewn:
Manylion Llyfryddiaeth
Prif Awdur: Held, Harald
Fformat: Livre numérique
Iaith:Anglais
Cyhoeddwyd: Wiesbaden : Vieweg+Teubner Verlag [20..].
Cham : Springer Nature
Rhifyn:1st ed. 2009.
Cyfres:Stochastic Programming
Mynediad Ar-lein:Accès sur la plateforme de l'éditeur
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Nodyn: Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Shape Optimization under Uncertainty from a Stochastic Programming Point of View, Texte imprimé, 9783834809094
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245 1 0 |a Shape Optimization under Uncertainty from a Stochastic Programming Point of View   |c by Harald Held. 
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500 |a Archives Springer e-books (Licence nationale) 
505 1 |a Solution of the Elasticity PDE Stochastic Programming Perspective Solving Shape Optimization Problems Numerical Results. 
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506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a Optimization problems are relevant in many areas of technical, industrial, and economic applications. At the same time, they pose challenging mathematical research problems in numerical analysis and optimization. Harald Held considers an elastic body subjected to uncertain internal and external forces. Since simply averaging the possible loadings will result in a structure that might not be robust for the individual loadings, he uses techniques from level set based shape optimization and two-stage stochastic programming. Taking advantage of the PDE's linearity, he is able to compute solutions for an arbitrary number of scenarios without significantly increasing the computational effort. The author applies a gradient method using the shape derivative and the topological gradient to minimize, e.g., the compliance . and shows that the obtained solutions strongly depend on the initial guess, in particular its topology. The stochastic programming perspective also allows incorporating risk measures into the model which might be a more appropriate objective in many practical applications. 
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