Details
| Original language | English |
|---|---|
| Title of host publication | Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016 |
| Subtitle of host publication | Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016 |
| Editors | Lesley Walls, Matthew Revie, Tim Bedford |
| Pages | 50 |
| Number of pages | 1 |
| Publication status | Published - 2017 |
Abstract
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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Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016. ed. / Lesley Walls; Matthew Revie; Tim Bedford. 2017. p. 50.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Bayesian model calibration using subset simulation
AU - Gong, Z.T.
AU - DiazDelaO, F.A.
AU - Beer, M.
N1 - Publisher Copyright: © 2017 Taylor & Francis Group, London.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85016194023&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781138029972
SP - 50
BT - Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016
A2 - Walls, Lesley
A2 - Revie, Matthew
A2 - Bedford, Tim
ER -