Perspectives on the integration between first-principles and data-driven modeling
Perspectives on the integration between first-principles and data-driven modeling
复制标题
第一性原理与数据驱动建模之间集成的观点
DOI：
10.1016/j.compchemeng.2022.107898
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发表时间：
2022
影响因子：
4.3
通讯作者：
Boukouvala, Fani
中科院分区：
文献类型：
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作者：
Bradley, William;Kim, Jinhyeun;Kilwein, Zachary;Blakely, Logan;Eydenberg, Michael;Jalvin, Jordan;Laird, Carl;Boukouvala, Fani
如果希望同时利用工程原理和数据科学，那么有效地嵌入和/或集成机械信息与数据驱动模型是必不可少的。杂交的机会出现在许多情况下，例如开发精确高保真计算机模型的更快模型;校正不能完全捕获系统物理现象的机械模型;或在机械模型中集成近似未知相关性的数据驱动组件。与此同时，在不同的文献中已经提出并应用了不同的技术来实现这种混合，例如混合建模，物理信息机器学习（ML）和模型校准。在本文中，我们回顾了这三个研究领域的方法，挑战，应用和算法，并讨论它们在不同的杂交场景的背景下。此外，我们提供了一个全面的比较杂交技术方面的差异和相似之处，以及优势和局限性和未来的前景。最后，我们通过一个化学反应器案例研究应用并说明了混合建模、物理信息ML和模型校准。
Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.