Large-Scale PDE-Constrained Optimization in Applications (Lecture Notes in Applied and Computational Mechanics (49))

Hazra, Subhendu Bikash

ISBN 10: 3642263887 / ISBN 13: 9783642263880

Edité par Springer, 2012

A propos de cet article

Like New.

N° de réf. du vendeur ERICA77336422638876

Détails bibliographiques

• Titre: Large-Scale PDE-Constrained Optimization in Applications (Lecture Notes in Applied and Computational Mechanics (49))
• Éditeur: Springer
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 3642263887
• ISBN à 13 chiffres: 9783642263880
• Reliure: Paperback
• État de l'article: Like New
• Type de livre: book

A propos de ce titre

Synopsis

With continuous development of modern computing hardware and applicable - merical methods, computational ?uid dynamics (CFD) has reached certain level of maturity so that it is being used routinely by scientists and engineers for ?uid ?ow analysis. Since most of the real-life applications involve some kind of optimization, it has been natural to extend the use of CFD tools from ?ow simulation to simu- tion based optimization. However, the transition from simulation to optimization is not straight forward, it requires proper interaction between advanced CFD meth- ologies and state-of-the-art optimization algorithms. The ultimate goal is to achieve optimal solution at the cost of few ?ow solutions. There is growing number of - search activities to achieve this goal. This book results from my work done on simulation based optimization problems at the Department of Mathematics, University of Trier, and reported in my postd- toral thesis (”Habilitationsschrift”) accepted by the Faculty-IV of this University in 2008. The focus of the work has been to develop mathematical methods and - gorithms which lead to ef?cient and high performance computational techniques to solve such optimization problems in real-life applications. Systematic development of the methods and algorithms are presented here. Practical aspects of implemen- tions are discussed at each level as the complexity of the problems increase, suppo- ing with enough number of computational examples.