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Publication Detail
Bayesian model calibration using subset simulation
  • Publication Type:
  • Authors:
    Gong ZT, DiazDelaO FA, Beer M
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  • Published proceedings:
    Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016
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  • Name of conference:
    26th European Safety and Reliability Conference, ESREL 2016
  • Conference place:
    Glasgow, UK
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© 2017 Taylor & Francis Group, London. Complex engineering systems are usually investigated by running expensive computer models. However, high-dimensional input and computational cost often hinder the calibration of such models. History matching is a calibration strategy, which has been successfully applied in many disciplines. It starts with a specific observation of a physical system, then sequentially discards the implausible input set, that is, the range of input values that do not provide a good fit between the model output and the available data. The computational efficiency depends on Bayesian emulation, which provides a statistical approximation to the model output. At each iteration, the non-implausible domain is sampled to run the simulator until it finds the matching input domain. Because the non-implausible domain can be significantly smaller than the original space, this paper proposes to use an engineering reliability method: Subset Simulation. This strategy uses Markov Chain Monte Carlo to sample from a failure domain. Numerical examples are provided to show the potential of this sampling scheme for model calibration.
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