在流体混合物热力学建模中结合机器学习与物理知识。

IF 7.6 2区 工程技术 Q1 CHEMISTRY, APPLIED
Fabian Jirasek, Hans Hasse
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引用次数: 0

摘要

流体混合物的热物理特性在许多科学和工程领域都非常重要。然而,该领域的实验数据很少,因此预测方法至关重要。目前已有不同类型的物理预测方法,包括分子模型、状态方程和过剩特性模型。目前,机器学习(ML)领域的新方法正在对这些成熟的方法进行补充。本综述侧重于这两种方法之间迅速发展的接口,并对物理建模和 ML 如何结合以产生混合模型进行了结构化概述。我们以近期研究的实例说明了不同的选择,并对未来发展进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.

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来源期刊
Annual review of chemical and biomolecular engineering
Annual review of chemical and biomolecular engineering CHEMISTRY, APPLIED-ENGINEERING, CHEMICAL
CiteScore
16.00
自引率
0.00%
发文量
25
期刊介绍: The Annual Review of Chemical and Biomolecular Engineering aims to provide a perspective on the broad field of chemical (and related) engineering. The journal draws from disciplines as diverse as biology, physics, and engineering, with development of chemical products and processes as the unifying theme.
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