Dimensionally-consistent equation discovery through probabilistic attribute grammars

Brence, Jure (avtor), Džeroski, Sašo (avtor), Todorovski, Ljupčo (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: equation discovery, symbolic regression, dimensional analysis, units of measurement, background knowledge, background information, computational scientific discovery
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: Str. 742-756
• Številčenje: Vol. 632
• PID: 20.500.12556/RUL-148320-915b6af9-8f94-252b-de38-5ea24ece57b2
• UDK: 004
• ISSN pri članku: 1872-6291
• DOI: 10.1016/j.ins.2023.03.073
• COBISS.SI-ID: 151276803
• Datum objave v RUL: 11.08.2023

Gradivo je del revije

• Naslov: Information sciences
• Založnik: Elsevier
• ISSN: 1872-6291
• COBISS.SI-ID: 23178245

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0103
• Naslov: Tehnologije znanja

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: N2-0128
• Naslov: Avtomatizirana sinteza in analiza znanstvenih modelov