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  • Dimensionally-consistent equation discovery through probabilistic attribute grammars [Elektronski vir]
    Brence, Jure ; Džeroski, Sašo ; Todorovski, Ljupčo
    Equation discovery, also known as symbolic regression, is a machine learning task of inducing closed-form equations from data and background knowledge. The latter takes various forms. Domain-specific ... knowledge can constrain the space of candidate equations to those that make sense in the scientific or engineering domain of use. Cross-domain knowledge, on the other hand, imposes general rules for model acceptability, such as parsimony, understandability, or consistency of the equations with the dimensional units of the variables. In this paper, we propose using attribute grammars to ensure the induced equations' dimensional consistency. Attribute grammars are flexible enough to combine cross-domain knowledge on dimensional consistency with domain-specific knowledge expressed as a probabilistic context-free grammar. At the same time, we show that attribute grammars can be efficiently transformed into probabilistic context-free grammars for equation discovery with existing algorithms. Finally, we provide empirical evidence that attribute grammars ensuring dimensional consistency of equations can significantly improve the performance of equation discovery on the standard set of a hundred Feynman benchmarks.
    Source: Information Sciences [Elektronski vir]. - ISSN 1872-6291 (Vol. 632, Jun. 2023, str. 742-756)
    Type of material - e-article ; adult, serious
    Publish date - 2023
    Language - english
    COBISS.SI-ID - 151276803

    Link(s):

    https://www.sciencedirect.com/science/article/pii/S0020025523003705?via%3Dihub#bl0010
    Repository of the University of Ljubljana – RUL
    DOI

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