Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

中文翻译：

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ASAMS：用于人工智能替代模型的自适应顺序采样和自动模型选择。

代理建模（SM）通常用于减少耗时的系统仿真的计算负担。但是，人工智能（AI）的不断进步和嵌入式传感器的普及导致创建了Digital Twins（DT），设计挖掘（DM）和软传感器（SS）。这些方法代表了替代模型的生成所面临的新挑战，因为它们需要实施精细的人工智能算法并最小化所测物理实验的数量。为了减少对物理系统的评估，已经开发了几种现有的自适应顺序采样方法。但是，它们在很大程度上受限于Kriging模型和基于Kriging模型的Monte Carlo Simulation。在本文中，我们将独特的自适应采样方法与自动机器学习方法（AutoML）集成在一起，以帮助进行模型选择，同时最大程度地减少基于人工智能算法的替代模型的系统评估和系统性能。在每次迭代中，此框架都使用网格搜索算法来确定最佳候选模型，并执行留一法交叉验证以计算每个采样点的性能。应用Voronoi图将采样区域划分为一些局部单元，并将Voronoi顶点视为新的候选点。样本点的性能用于估计一组候选点的模型的准确性，以选择那些将进一步提高模型准确性的样本。然后，候选模型的数量减少了。最后，使用两个示例对框架的性能进行了测试，以证明所提出方法的适用性。