An end-to-end deep learning approach for extracting stochastic dynamical systems with $\alpha$-stable L\'evy noise

• URL: http://arxiv.org/abs/2201.13114v1
• Date: Mon, 31 Jan 2022 10:51:25 GMT
• Title: An end-to-end deep learning approach for extracting stochastic dynamical systems with $\alpha$-stable L\'evy noise
• Authors: Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan
• Abstract summary: In this work, we identify dynamical systems driven by $alpha$-stable Levy noise from only random pairwise data. Our innovations include: (1) designing a deep learning approach to learn both drift and diffusion terms for Levy induced noise with $alpha$ across all values, (2) learning complex multiplicative noise without restrictions on small noise intensity, and (3) proposing an end-to-end complete framework for systems identification.
• Score: 5.815325960286111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields. Moreover, a growing amount of research work tends to transfer deterministic dynamical systems to stochastic dynamical systems, especially those driven by non-Gaussian multiplicative noise. However, lots of log-likelihood based algorithms that work well for Gaussian cases cannot be directly extended to non-Gaussian scenarios which could have high error and low convergence issues. In this work, we overcome some of these challenges and identify stochastic dynamical systems driven by $\alpha$-stable L\'evy noise from only random pairwise data. Our innovations include: (1) designing a deep learning approach to learn both drift and diffusion terms for L\'evy induced noise with $\alpha$ across all values, (2) learning complex multiplicative noise without restrictions on small noise intensity, (3) proposing an end-to-end complete framework for stochastic systems identification under a general input data assumption, that is, $\alpha$-stable random variable. Finally, numerical experiments and comparisons with the non-local Kramers-Moyal formulas with moment generating function confirm the effectiveness of our method.

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