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• W2185118385 abstract "Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate-based opti- mization algorithm that uses a trust region-based sequential approximate optimization (SAO) framework and a statis- tical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using tech- niques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques— full factorial (FF), Latin hypercube sampling, and central composite design—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required data- base. As the number of design variables grows, the com- putational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodol- ogy dramatically reduces the total number of calls to the expensive simulation runs during the optimization process." @default.
• W2185118385 created "2016-06-24" @default.
• W2185118385 creator A5050111950 @default.
• W2185118385 date "2010-01-01" @default.
• W2185118385 modified "2023-09-27" @default.
• W2185118385 title "Data driven surrogate-based optimization in the problem solving environment WBCSim" @default.
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• W2185118385 hasPublicationYear "2010" @default.
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