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arXiv · 2409.15167

Data-driven model discovery with Kolmogorov-Arnold networks

Mohammadamin Moradi·Shirin Panahi·Erik M. Bollt·Ying-Cheng Lai
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Abstract

Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large variety of ecosystems. Exploiting the recently articulated Kolmogorov-Arnold networks, we develop a general model-discovery framework for any dynamical systems including those that do not satisfy the sparsity condition. In particular, we demonstrate non-uniqueness in that a large number of approximate models of the system can be found which generate the same invariant set with the correct statistics such as the Lyapunov exponents and Kullback-Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.

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Mohammadamin Moradi, Shirin Panahi, Erik M. Bollt, Ying-Cheng Lai. 2024-09-23. Data-driven model discovery with Kolmogorov-Arnold networks. https://arxiv.org/abs/2409.15167

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