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.

中文翻译：

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一种新的基于自适应采样的方法，用于利用人工神经网络识别计算密集型模型的可行区域

在多元过程中，确定可行的操作区域是几个领域关注的问题。当过程模型本质上是黑匣子和/或在计算上昂贵时，这尤其具有挑战性，因为没有可用的分析解决方案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，在其中评估模型以开发计算上有效的替代模型。在这项工作中，提出了一种基于人工神经网络的替代模型，该模型与基于统计的方法（杰克·肯尼夫（Jack-knifing））相集成，以估计替代模型预测的方差。这允许实施自适应采样方法，其中在可行区域边界附近或预测不确定性高的区域中识别新样本。对于不同维度的案例研究，所提出的方法比以前基于kriging的方法表现更好。