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An unsupervised data completion method for physically-based data-driven models

机译:基于物理的数据驱动模型的无监督数据完成方法

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Data-driven methods are an innovative model-free approach for engineering and sciences, still in process of maturation. The idea behind is the combination of data analytics techniques, to handle the huge amount of data derived from continuous monitoring or experimental measurements, and of the constraints imposed by universal physical laws, particular to the field in hands. A well-known problem in the former corresponds to the quality and completeness of the available data that, sometimes, are so poor that make the predictions useless. In data-driven simulation-based engineering and sciences (DDSBES), the intrinsic physical constraints may help in completing the missing data in a more precise manner, by forcing them to remain in the manifold defined by the physical laws. In this work, a suitable imputation method to complete incomplete data that preserves the data context-dependent structure is presented. This is accomplished by enforcing the data to fulfill the set of physical constraints, specific to the problem. For this purpose, a generalization of the weighted mean concept is proposed, where the distance to the admissible points (in a physical sense) is used as a weighting function to get the optimal candidate. The method is evaluated in a classical regression problem, where it is compared with other standard methods, showing better results. Then, its application is illustrated in two data-driven problems, where no filling data procedure has been yet proposed, showing good predictive capability, provided that the data are close enough to the actual system state. (C) 2018 Elsevier B.V. All rights reserved.
机译:数据驱动方法是工程和科学领域的一种创新的无模型方法,仍处于成熟阶段。背后的想法是数据分析技术的组合,用于处理从连续监视或实验测量中获得的大量数据,以及通用物理定律(尤其是手头领域)施加的约束。前者中的一个众所周知的问题与可用数据的质量和完整性相对应,有时这些数据是如此之差以至于无法进行预测。在基于数据的基于仿真的工程和科学(DDSBES)中,内在的物理约束可能会通过迫使它们保留在物理定律所定义的多方面来帮助以更精确的方式完成丢失的数据。在这项工作中,提出了一种适合的插补方法来完成不完整的数据,该方法保留了数据上下文相关的结构。这是通过强制数据满足特定于该问题的一组物理约束来实现的。为此,提出了加权均值概念的一般化方法,其中到允许点的距离(在物理意义上)用作加权函数以获得最佳候选值。在经典回归问题中对该方法进行了评估,并将其与其他标准方法进行比较,显示了更好的结果。然后,在两个数据驱动的问题中说明了它的应用,其中,如果数据足够接近实际系统状态,则尚未提出填充数据过程,该过程显示出良好的预测能力。 (C)2018 Elsevier B.V.保留所有权利。

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