20.500.12556/DiRROS-21779 Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data Ordinary differential equations (ODEs) are a widely used formalism for the mathematical modeling of dynamical systems, a task omnipresent in scientific domains. The paper introduces a novel method for inferring ODEs from data, which extends ProGED, a method for equation discovery that allows users to formalize domain-specific knowledge as probabilistic context-free grammars and use it for constraining the space of candidate equations. The extended method can discover ODEs from partial observations of dynamical systems, where only a subset of state variables can be observed. To evaluate the performance of the newly proposed method, we perform a systematic empirical comparison with alternative state-of-the-art methods for equation discovery and system identification from complete and partial observations. The comparison uses Dynobench, a set of ten dynamical systems that extends the standard Strogatz benchmark. We compare the ability of the considered methods to reconstruct the known ODEs from synthetic data simulated at different temporal resolutions. We also consider data with different levels of noise, i.e., signal-to-noise ratios. The improved ProGED compares favourably to state-of-the-art methods for inferring ODEs from data regarding reconstruction abilities and robustness to data coarseness, noise, and completeness. ordinary differential equations equation discovery mathematical modeling system identification symbolic regression partial observability odkrivanje enačb true false true Springer Nature Švica Angleški jezik Slovenski jezik © The Author(s) 2024 Neznano 2025-03-27 14:38:15 2025-03-27 14:38:16 2025-10-24 03:49:48 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Soavtorji: Boštjan Gec, Jure Brence, Ljupčo Todorovski, Sašo Džeroski; Opis vira z dne 27. 3. 2025; str. 7689-7721 iss. 10 Vol. 113 Oct. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-03-13 2024-02-14 2024-05-29 004.8 1573-0565 10.1007/s10994-024-06522-1 230493443 https://link.springer.com/article/10.1007/s10994-024-06522-1 1 bda7a774-0b10-11f0-963a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=31386 RAZ_Omejc_Nina_2024.pdf RAZ_Omejc_Nina_2024.pdf 1 E6F3C90B60B1FB137EC01290BAD0411D e84304f6b6481353a2435d8654ee4d404c53576523c4ffbf218159f09348df9f ac8c732e-0b11-11f0-963a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=31388 Institut Jožef Stefan 0 0 0