A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network
A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network
复制标题
一种基于自适应采样的新型方法，用于使用人工神经网络识别计算密集型模型的可行区域
DOI：
10.1002/aic.17095
复制
发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
M. Ierapetritou
中科院分区：
文献类型：
--
作者：
N. Metta;R. Ramachandran;M. Ierapetritou
多变量过程操作可行域的确定是多个领域的研究热点。当过程模型本质上是黑箱和/或计算昂贵时，这尤其具有挑战性，因为分析解决方案不可用，并且可能的模型评估数量有限。需要一种有效的方法来识别样本，在这些样本中对模型进行评估，以开发计算效率高的替代模型。在这项工作中，提出了一种基于人工神经网络的代理模型，它与基于统计的方法（Jack‐knifing）相结合，以估计代理模型预测的方差。这允许实现自适应采样方法，其中新样本被识别为接近可行区域边界或在高预测不确定性的区域中。所提出的方法比以前发表的克里格为基础的方法，不同的维度的案例研究。
Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black‐box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical‐based approach (Jack‐knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.