20.500.12556/RUL-148320 Dimensionally-consistent equation discovery through probabilistic attribute grammars 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. equation discovery symbolic regression dimensional analysis units of measurement background knowledge background information computational scientific discovery true false true Angleški jezik Ni določen Članek v reviji 2023-08-11 14:16:26 2023-08-11 14:16:28 2025-08-08 13:22:32 0000-00-00 00:00:00 2023 0 0 Str. 742-756 Vol. 632 Jun. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004 1872-6291 10.1016/j.ins.2023.03.073 151276803 1-s2.0-S0020025523003705-main.pdf 1-s2.0-S0020025523003705-main.pdf 1 CDA3F14DF1AB142B1DD9ABD9201E7A99 833c038fec4d33bad07fafece3a62bd318c6f61537154ac285bf3057c42987fb 373d5124-3841-11ee-b9d7-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=172435 https://www.sciencedirect.com/science/article/pii/S0020025523003705 0 e4871dbc-3840-11ee-b9d7-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=172434 Fakulteta za matematiko in fiziko 0 0 0