Abstract
Ordinary differential equations (ODEs) are a widely used formalism for mathematical modeling of dynamical systems, a task omnipresent in many scientific domains. The paper introduces a novel method for inferring ODEs from data. It extends ProGED, a method for equation discovery that employs probabilistic context-free grammars for constraining the space of candidate equations. The proposed method can discover ODEs from partial observations of dynamical systems, where only a subset of state variables can be observed. The new method’s empirical evaluation shows it can reconstruct the ODEs of the well-known Van der Pol oscillator from synthetic simulation data. In terms of reconstruction performance, improved ProGED compares favorably to state-of-the-art methods for inferring ODEs from data.
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Acknowledgements
The authors acknowledge the financial support of the Slovenian Research Agency via the research core funding No. P2-0103 and project No. N2-0128.
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Gec, B., Omejc, N., Brence, J., Džeroski, S., Todorovski, L. (2022). Discovery of Differential Equations Using Probabilistic Grammars. In: Pascal, P., Ienco, D. (eds) Discovery Science. DS 2022. Lecture Notes in Computer Science(), vol 13601. Springer, Cham. https://doi.org/10.1007/978-3-031-18840-4_2
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