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arXiv · 2501.13301

A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems

Yuanchao Xu·Kaidi Shao·Isao Ishikawa·Yuka Hashimoto·Nikos Logothetis·Zhongwei Shen
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Abstract

We present Stochastic Dynamic Mode Decomposition (SDMD), a novel data-driven framework for approximating the Koopman semigroup in stochastic dynamical systems. Unlike existing methods, SDMD explicitly incorporates sampling time into its approximation, ensuring numerical stability and precision. By directly approximating the Koopman semigroup instead of the generator, SDMD avoids computationally expensive matrix exponential computations, which offers a more efficient and practical pathway for analyzing stochastic dynamics. The framework further integrates neural networks to automate basis selection, which reduces the reliance on manual intervention while maintaining computational efficiency. Rigorous theoretical guarantees, including convergence in the large data limit, zero-limit of sampling time, and large dictionary size, establish the method's reliability. Numerical experiments on canonical stochastic systems validate SDMD's effectiveness in approximating eigenvalues and eigenfunctions of the stochastic Koopman operator.

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Yuanchao Xu, Kaidi Shao, Isao Ishikawa, Yuka Hashimoto, Nikos Logothetis, Zhongwei Shen. 2025-01-23. A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems. https://arxiv.org/abs/2501.13301

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