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Reliable Real-Time Optimization of Nonconvex Systems Described by Parametrized Partial Differential Equations

The solution of a single optimization problem often requires computationally-demanding evaluations; this is especially true in optimal design of engineering components and systems described by partial differential equations. We present a technique for the rapid and reliable optimization of systems characterized by linear-functional outputs of partial differential equations with affine parameter dependence. The critical ingredients of the method are: (i) reduced-basis techniques for dimension reduction in computational requirements; (ii) an "off-line/on-line" computational decomposition for the rapid calculation of outputs of interest and respective sensitivities in the limit of many queries; (iii) a posteriori error bounds for rigorous uncertainty and feasibility control; (iv) Interior Point Methods (IPMs) for efficient solution of the optimization problem; and (v) a trust-region Sequential Quadratic Programming (SQP) interpretation of IPMs for treatment of possibly non-convex costs and constraints. / Singapore-MIT Alliance (SMA)

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/3707
Date01 1900
CreatorsOliveira, I.B., Patera, Anthony T.
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
TypeArticle
Format334725 bytes, application/pdf
RelationHigh Performance Computation for Engineered Systems (HPCES);

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