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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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