Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 30)
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Table of contents (20 papers)
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Introduction
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Large-Scale CFD Applications
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Multifidelity Models and Inexactness
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Sensitivities for PDE-based Optimization
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NLP Algorithms and Inequality Constraints
Keywords
About this book
Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.
Editors and Affiliations
Bibliographic Information
Book Title: Large-Scale PDE-Constrained Optimization
Editors: Lorenz T. Biegler, Matthias Heinkenschloss, Omar Ghattas, Bart Bloemen Waanders
Series Title: Lecture Notes in Computational Science and Engineering
DOI: https://doi.org/10.1007/978-3-642-55508-4
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2003
Softcover ISBN: 978-3-540-05045-2Published: 05 September 2003
eBook ISBN: 978-3-642-55508-4Published: 06 December 2012
Series ISSN: 1439-7358
Series E-ISSN: 2197-7100
Edition Number: 1
Number of Pages: VI, 349
Number of Illustrations: 13 b/w illustrations, 12 illustrations in colour
Topics: Analysis, Computational Mathematics and Numerical Analysis, Optimization, Computational Science and Engineering, Partial Differential Equations