Data-driven identification of interpretable reduced-order models using sparse regression
Data-driven identification of interpretable reduced-order models using sparse regression
复制标题
使用稀疏回归数据驱动识别可解释的降阶模型
DOI：
10.1016/j.compchemeng.2018.08.010
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. Kwon
中科院分区：
文献类型：
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作者：
Abhinav Narasingam;J. Kwon
开发物理可解释的降阶模型（ROM）至关重要，因为它们除了提供许多化学过程的计算易处理性之外，还提供了对潜在现象的理解。在这项工作中，我们从回归的角度重新设想了非线性动力系统的模型简化。特别是，我们解决了一大组候选函数形式的稀疏回归问题，以确定 ROM 的结构。该方法通过选择稀疏模型来平衡模型的复杂性和准确性，该稀疏模型可以避免过度拟合，从而在遭受不同输入配置文件时准确地表示系统动态。通过应用于水力压裂过程，我们证明了所开发的模型能够揭示重要的物理现象，例如支撑剂输运和裂缝内部的裂缝扩展。它还强调了如何将先验知识轻松地融入到算法中，并产生用于控制器综合的精确 ROM。
Developing physically interpretable reduced-order models (ROMs) is critical as they provide an understanding of the underlying phenomena apart from computational tractability for many chemical processes. In this work, we re-envision the model reduction of nonlinear dynamical systems from the perspective of regression. In particular, we solve a sparse regression problem over a large set of candidate functional forms to determine the structure of the ROM. The method balances model complexity and accuracy by selecting a sparse model that avoids overfitting to accurately represent the system dynamics when subjected to a different input profile. By applying to a hydraulic fracturing process, we demonstrate the ability of the developed models to reveal important physical phenomena such as proppant transport and fracture propagation inside a fracture. It also highlights how a priori knowledge can be incorporated easily into the algorithm and results in accurate ROMs that are used for controller synthesis.