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Identification of quasi-optimal regions in the design space using surrogate modeling

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Abstract

The use of Surrogate Based Optimization (SBO) is widely spread in engineering design to find optimal performance characteristics of expensive simulations (forward analysis: from input to optimal output). However, often the practitioner knows a priori the desired performance and is interested in finding the associated input parameters (reverse analysis: from desired output to input). A popular method to solve such reverse (inverse) problems is to minimize the error between the simulated performance and the desired goal. However, there might be multiple quasi-optimal solutions to the problem. In this paper, the authors propose a novel method to efficiently solve inverse problems and to sample Quasi-Optimal Regions (QORs) in the input (design) space more densely. The development of this technique, based on the probability of improvement criterion and kriging models, is driven by a real-life problem from bio-mechanics, i.e., determining the elasticity of the (rabbit) tympanic membrane, a membrane that converts acoustic sound wave into vibrations of the middle ear ossicular bones.

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Acknowlegments

Ivo Couckuyt and Jef Aernouts are funded by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)

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Authors and Affiliations

  1. Department of Information Technology (INTEC), Ghent University-IBBT, Gaston Crommenlaan 8, Ghent, 9050, Belgium

    Ivo Couckuyt, Dirk Deschrijver, Filip De Turck & Tom Dhaene

  2. Laboratory of Biomedical Physics, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium

    Jef Aernouts

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  1. Ivo Couckuyt
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  2. Jef Aernouts
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  3. Dirk Deschrijver
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  4. Filip De Turck
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  5. Tom Dhaene
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Correspondence to Ivo Couckuyt.

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Couckuyt, I., Aernouts, J., Deschrijver, D. et al. Identification of quasi-optimal regions in the design space using surrogate modeling. Engineering with Computers 29, 127–138 (2013). https://doi.org/10.1007/s00366-011-0249-3

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  • DOI: https://doi.org/10.1007/s00366-011-0249-3

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