On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models
On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models
复制标题
通过实验数据对计算机模型混合物进行贝叶斯校准，以及预测模型的设计
DOI：
10.1016/j.jcp.2017.04.003
复制
发表时间：
2017
影响因子：
4.1
通讯作者：
Lin, Guang
中科院分区：
文献类型：
--
作者：
Karagiannis, Georgios;Lin, Guang
对于许多实际系统，可能存在具有不同物理和预测能力的多种计算机模型。为了实现更准确的模拟/预测，需要对这些模型进行适当的组合和校准。我们提出了计算机模型混合方法的贝叶斯校准，该方法依赖于将真实系统输出表示为可用计算机模型输出与未知输入相关权重函数的混合的思想。该方法通过组合、加权和校准贝叶斯框架中的可用模型，构建完全贝叶斯预测模型作为真实系统输出的模拟器。此外，它适合校准计算机模型的混合，领域科学家可以使用这些模型作为一种手段，以灵活和有原则的方式组合可用的计算机模型，并执行可靠的模拟。它可以解决现实情况，其中一个模型在不同的输入值下可能比其他模型更准确，因为指示每个模型的贡献的混合权重是输入的函数。对校准参数的推断可以考虑与不同物理场相关的多个计算机模型。该方法不需要了解模型的保真度阶。由于考虑了适合混合模型框架的多个计算机模型，我们提供了一种能够减轻计算开销的技术。我们在涉及天气研究和预报大规模气候模型的实际应用中实施了所提出的方法。
For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.
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DOI：
--
发表时间：
1971
期刊：
影响因子：
--
作者：
V. Turchin
通讯作者：
V. Turchin
DOI：
10.1080/01621459.2017.1295863
发表时间：
2014-11
影响因子：
3.7
作者：
K. S. Bhat;D. Mebane;P. Mahapatra;C. Storlie
通讯作者：
K. S. Bhat;D. Mebane;P. Mahapatra;C. Storlie
影响因子：
2.2
作者：
Gramacy, Robert B.;Lee, Herbert K. H.
通讯作者：
Lee, Herbert K. H.
美国历史气候网 (US HCN) 每月气温和降水数据
DOI：
--
发表时间：
1996
期刊：
影响因子：
--
作者：
R. C. Daniels;T. Boden;D. Easterling;T. Karl;E. H. Mason;P. Hughes;D. Bowman
通讯作者：
D. Bowman
影响因子：
3.2
作者：
Chen, F;Dudhia, J
通讯作者：
Dudhia, J