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Ensemble Bayesian method for parameter distribution inference: application to reactor physics
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DOI：10.1007/s41365-023-01356-3.png)
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The estimation of model parameters is an important subject in engineering. In this area of work, the prevailing approach is to estimate or calculate these as deterministic parameters. In this study, we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem. Under this framework, we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo (MCMC) method. Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively, even for several correlated parameters simultaneously. Our experiments include cases of engineering software calls, demonstrating that the method can be applied to engineering, such as nuclear reactor engineering.
Keyword:
Model parameters
Bayesian inference
Frequency distribution
Ensemble Bayesian method
KL divergence
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3.8
论文数:
2.1K
被引数:
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