| Title: | Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data |
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| Authors: | ID Omejc, Nina, Institut "Jožef Stefan" (Author)
ID Gec, Boštjan, Institut "Jožef Stefan" (Author)
ID Brence, Jure, Institut "Jožef Stefan" (Author)
ID Todorovski, Ljupčo, Institut "Jožef Stefan" (Author)
ID Džeroski, Sašo, Institut "Jožef Stefan" (Author) |
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| Files: |  URL - Source URL, visit https://link.springer.com/article/10.1007/s10994-024-06522-1
 PDF - Presentation file, download (2,66 MB)
MD5: E6F3C90B60B1FB137EC01290BAD0411D
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | 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. |
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| Keywords: | ordinary differential equations, equation discovery, mathematical modeling, system identification, symbolic regression, partial observability |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 13.03.2023 |
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| Article acceptance date: | 14.02.2024 |
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| Publication date: | 29.05.2024 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 7689-7721 |
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| Numbering: | Vol. 113, iss. 10 |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-21779  |
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| UDC: | 004.8 |
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| ISSN on article: | 1573-0565 |
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| DOI: | 10.1007/s10994-024-06522-1 |
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| COBISS.SI-ID: | 230493443 |
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| Copyright: | © The Author(s) 2024 |
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| Note: | Nasl. z nasl. zaslona;
Soavtorji: Boštjan Gec, Jure Brence, Ljupčo Todorovski, Sašo Džeroski;
Opis vira z dne 27. 3. 2025;
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| Publication date in DiRROS: | 27.03.2025 |
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| Views: | 595 |
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| Downloads: | 341 |
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