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Learning Effective Good Variables from Physical Data
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DOI：10.3390/make6030077.png)
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摘要
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We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test two machine learning approaches to discover possible groups or combinations of primitive variables, regardless of data origin, being it numerical or experimental: the first approach is based on regression models, whereas the second on classification models. The variable group (here referred to as the new effective good variable) can be considered as successfully found when the physical property of interest is characterized by the following effective invariant behavior: in the first method, invariance of the group implies invariance of the property up to a given accuracy; in the other method, upon partition of the physical property values into two or more classes, invariance of the group implies invariance of the class. For the sake of illustration, the two methods are successfully applied to two popular empirical correlations describing the convective heat transfer phenomenon and to the Newton's law of universal gravitation.
Keyword:
machine learning in physics
primitive variable analysis
physical property invariance
feature grouping
期刊
M
IF:
6
论文数:
841
被引数:
1.8K
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems通过非线性动力系统的稀疏识别从数据中发现控制方程
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