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Titlebook: Large-Scale PDE-Constrained Optimization; Lorenz T. Biegler,Matthias Heinkenschloss,Bart Blo Conference proceedings 2003 Springer-Verlag B

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書目名稱Large-Scale PDE-Constrained Optimization
編輯Lorenz T. Biegler,Matthias Heinkenschloss,Bart Blo
視頻videohttp://file.papertrans.cn/582/581402/581402.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computational Science and Engineering
圖書封面Titlebook: Large-Scale PDE-Constrained Optimization;  Lorenz T. Biegler,Matthias Heinkenschloss,Bart Blo Conference proceedings 2003 Springer-Verlag B
描述.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..
出版日期Conference proceedings 2003
關(guān)鍵詞PDE-constrained optimization; inverse problems; large-scale optimization; optimal control; optimal desig
版次1
doihttps://doi.org/10.1007/978-3-642-55508-4
isbn_softcover978-3-540-05045-2
isbn_ebook978-3-642-55508-4Series ISSN 1439-7358 Series E-ISSN 2197-7100
issn_series 1439-7358
copyrightSpringer-Verlag Berlin Heidelberg 2003
The information of publication is updating

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Large-Scale PDE-Constrained Optimization: An Introduction known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. Recent advances in algorithms, software, and high performance computing systems have resulted in PDE simulations that can often scale to mi
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First-Order Approximation and Model Management in Optimization Our approach tries to alleviate the expense of relying exclusively on high-fidelity simulations, e.g., the solution of the governing differential equations on very fine meshes or the use of very detailed physics, while still guaranteeing global convergence of the overall optimization process to a s
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Multifidelity Global Optimization Using DIRECTimensionality. The DIRECT algorithm has been modified to incorporate multifidelity information. Two correction strategies have been employed with problems with up to ten dimensions and thousands of local optima. DIRECT has demonstrated the ability to reach the same global optimum using high fidelity
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