Segmented Bayesian Calibration of Multidisciplinary Models
| Autor: | Benjamin P. Smarslok, Erin C. DeCarlo, Sankaran Mahadevan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Kullback–Leibler divergence
Calibration (statistics) Aerospace Engineering Bayesian network Inference Bayes factor 02 engineering and technology Bayesian inference computer.software_genre 01 natural sciences Variable-order Bayesian network 010104 statistics & probability 020303 mechanical engineering & transports 0203 mechanical engineering Econometrics Data mining 0101 mathematics Divergence (statistics) computer |
| Zdroj: | AIAA Journal. 54:3727-3741 |
| ISSN: | 1533-385X 0001-1452 |
| DOI: | 10.2514/1.j054960 |
| Popis: | This paper investigates Bayesian model calibration for multidisciplinary problems that involve several disciplinary models and multiple sources of data regarding individual and combined physics. A segmented approach is explored as an alternative to simultaneous calibration of the parameters and discrepancy terms of all the component models. Simultaneous Bayesian calibration requires conducting inference on all uncertain parameters using all models and data concurrently. This can lead to significant computational burden and ambiguity regarding each individual model’s contribution to the overall prediction uncertainty. Segmented Bayesian model calibration is first investigated with two illustrative mathematical examples and the performance of this strategy is examined for different characteristics of the problem (i.e., model dependence and data availability). The Kullback–Leibler divergence and the Bayes factor metric are used to compare the computational effort and accuracy of the segmented and simultaneou... |
| Databáze: | OpenAIRE |
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