A Model Reification Approach to Fusing Information from Multifidelity Information Sources
A Model Reification Approach to Fusing Information from Multifidelity Information Sources
复制标题
融合多保真信息源信息的模型具体化方法
DOI：
10.2514/6.2017-1949
复制
发表时间：
2017
期刊：
影响因子：
2.5
通讯作者：
W. D. Thomison
中科院分区：
文献类型：
--
作者：
W. D. Thomison
虽然设计者可以使用的计算模型越来越多，可以解决许多问题，但它使正确利用每个模拟器提供的信息的过程变得复杂。选择精度或保真度最高的模型似乎很直观。决策者希望获得最大程度的确定性，以提高他们的效力。然而，高保真模型往往以高昂的计算成本为代价。虽然相对缺乏准确性，但低保真模型确实包含了某种程度的有用信息，这些信息可以以低成本获得。我们提出了一种利用这些信息来生成融合模型的方法，该模型具有比其任何组成模型更好的预测能力。我们的方法使用模型具体化方法来估计每个模型之间的相关性，该方法消除了对观测数据的要求。然后，在更新过程中使用该相关性，从而可以将来自多个模型的不确定输出融合在一起，以更好地估计感兴趣的一些数量。
While the growing number of computational models available to designers can solve a lot of problems, it complicates the process of properly utilizing the information provided by each simulator. It may seem intuitive to select the model with the highest accuracy, or fidelity. Decision makers want the greatest degree of certainty to increase their efficacy. However, high fidelity models often come at a high computational expense. While comparatively lacking in veracity, low fidelity models do contain some degree of useful information that can be obtained at a low cost. We propose a method to utilize this information to generate a fused model with superior predictive capability than any of its constituent models. Our methodology estimates the correlation between each model using a model reification approach that eliminates the observational data requirement. The correlation is then used in an updating procedure whereby uncertain outputs from multiple models may be fused together to better estimate some quantity or quantities of interest.
影响因子：
0.9
作者：
Goldstein, Michael;Rougier, Jonathan
通讯作者：
Rougier, Jonathan