Space partitioning in engineering design via metamodel acceptance score distribution

Hamad, Husam; Al-Smadi, Adnan

doi:10.1007/s00366-007-0056-z

Cited by 20 publications

(7 citation statements)

References 23 publications

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“…A way to vary the criteria dynamically with the sample selection loop would also be useful as is the study of transductive learning [86]. A possible integration with domain partitioning methods (e.g., as done in [41]) is also promising.…”

Section: Discussionmentioning

confidence: 99%

“…While this solves the numerical issue, the resulting error is an absolute-relative hybrid and becomes impossible to interpret. A different solution is to scale or translate the response to eliminate small absolute values (e.g., as proposed in [41]). However, the best scale factor is not always obvious and shifting the response can introduce its own problems.…”

Section: Relative Errorsmentioning

confidence: 99%

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Multiobjective global surrogate modeling, dealing with the 5-percent problem

Gorissen

Couckuyt

Laermans

et al. 2009

Engineering with Computers

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When dealing with computationally expensive simulation codes or process measurement data, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization, prototyping and optimization. Typically the model parameter (=hy-perparameter) optimization problem as part of global surrogate modeling is formulated in a single objective way. Models are generated according to a single objective (accuracy). However, this requires an engineer to determine a single accuracy target and measure upfront, which is hard to do if the behavior of the response is unknown. Likewise, the different outputs of a multi-output system are typically modeled separately by independent models. Again, a multiobjective approach would benefit the domain expert by giving information about output correlation and enabling automatic model type selection for each output dynamically. With this paper the authors attempt to increase awareness of the subtleties involved and discuss a number of solutions and applications. In particular we present a multiobjective framework for automatic global surrogate model generation to help tackle both problems and that is applicable in both the static and sequential design (adaptive sampling) case.

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Section: Discussionmentioning

confidence: 99%

Section: Relative Errorsmentioning

confidence: 99%

Multiobjective global surrogate modeling, dealing with the 5-percent problem

Gorissen

Couckuyt

Laermans

et al. 2009

Engineering with Computers

View full text Add to dashboard Cite

show abstract

“…) 1 200 7700 300 2 200 7900 340 3 220 7700 340 4 220 7900 300 5 210 7800 320 the optimization. In this study, three metrics, namely R-square, relative average absolute error (RAAE), and relative maximum absolute error (RMAE) [34,35,65] are used to assess the accuracy of surrogate models through the assessment point given in Table 4 as,…”

Section: Tablementioning

confidence: 99%

Multiobjective robust design optimization of fatigue life for a truck cab

Fang

Gao

Sun

et al. 2015

Reliability Engineering & System Safety

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“…To check the accuracy of the fitted model, the root mean square error (RMSE), R 2 and the relative error (RE) are applied to examine the accuracy of meta-model respectively, which can be written as [35][36][37]: …”

Section: Model Accuracy and Optimization Algorithmmentioning

confidence: 99%

Mean compressive stress constitutive equation and crashworthiness optimization design of three novel honeycombs under axial compression

Zhang

et al. 2015

International Journal of Mechanical Sciences

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Space partitioning in engineering design via metamodel acceptance score distribution

Cited by 20 publications

References 23 publications

Multiobjective global surrogate modeling, dealing with the 5-percent problem

Multiobjective global surrogate modeling, dealing with the 5-percent problem

Multiobjective robust design optimization of fatigue life for a truck cab

Mean compressive stress constitutive equation and crashworthiness optimization design of three novel honeycombs under axial compression

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