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Hosder, Serhat; Watson, Layne T.; Grossman, Bernard; Mason, William H.; Kim, Hongman; Haftka, Raphael T.; Cox, Steven

doi:10.1023/a:1016094522761

Cited by 76 publications

(1 citation statement)

References 23 publications

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“…As with other bilevel MDO architectures, the postoptimality analysis is necessary to ensure convergence to an optimal design of the original monolithic problem. We also note the work of Kaufman et al [197] and Hosder et al [198], who use structural optimization as an inner loop in aircraft design optimization with a surrogate model replacing the post-optimality sensitivity equations.…”

Section: J Asymmetric Subspace Optimization (Aso)mentioning

confidence: 95%

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Multidisciplinary design optimization (MDO) is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of MDO, various methods (architectures) have been developed and applied to solve MDO problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitates the understanding of the various architectures, and how they relate to each other.A classification of the MDO architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both a theoretical and experimental perspective. For each architecture, several applications to the solution of engineering design problems are cited. The result is a comprehensive but straightforward introduction to MDO for non-specialists, and a reference detailing all current MDO architectures for specialists.

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Section: J Asymmetric Subspace Optimization (Aso)mentioning

confidence: 95%

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Nonlinear variable selection algorithms for surrogate modeling

Zhai

Boukouvala

2019

AIChE Journal

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Having the ability to analyze, simulate, and optimize complex systems is becoming more important in all engineering disciplines. Decision-making using complex systems usually leads to nonlinear optimization problems, which rely on computationally expensive simulations. Therefore, it is often challenging to detect the actual structure of the optimization problem and formulate these problems with closed-form analytical expressions. Surrogate-based optimization of complex systems is a promising approach that is based on the concept of adaptively fitting and optimizing approximations of the input-output data. Standard surrogate-based optimization assumes the degrees of freedom are known a priori; however, in real applications the sparsity and the actual structure of the black-box formulation may not be known. In this work, we propose to select the correct variables contributing to each objective function and constraints of the black-box problem, by formulating the identification of the true sparsity of the formulation as a nonlinear feature selection problem. We compare three variable selection criteria based on Support Vector Regression and develop efficient algorithms to detect the sparsity of black-box formulations when only a limited amount of deterministic or noisy data is available. K E Y W O R D Snonlinear variable selection, surrogate modeling, support vector regression

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Generalized pointwise bias error bounds for response surface approximations

Goel

Haftka

Papila

et al. 2005

Numerical Meth Engineering

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SUMMARYThis paper proposes a generalized pointwise bias error bounds estimation method for polynomialbased response surface approximations when bias errors are substantial. A relaxation parameter is introduced to account for inconsistencies between the data and the assumed true model. The method is demonstrated with a polynomial example where the model is a quadratic polynomial while the true function is assumed to be cubic polynomial. The effect of relaxation parameter is studied. It is demonstrated that when bias errors dominate, the bias error bounds characterize the actual error field better than the standard error. The bias error bound estimates also help to identify regions in the design space where the accuracy of the response surface approximations is inadequate. It is demonstrated that this information can be utilized for adaptive sampling in order to improve accuracy in such regions.

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Untitled

Cited by 76 publications

References 23 publications

Multidisciplinary Design Optimization: A Survey of Architectures

Multidisciplinary Design Optimization: A Survey of Architectures

Nonlinear variable selection algorithms for surrogate modeling

Generalized pointwise bias error bounds for response surface approximations

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