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GenML: A Python Library to Generate the Mittag-Leffler Correlated Noise

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arxiv 2403.04273 v2 pith:5ZSZVXMU submitted 2024-03-07 cs.MS cond-mat.stat-mechphysics.comp-ph

Xiang Qu , Hui Zhao , Wenjie Cai , Gongyi Wang , Zihan Huang This is my paper · ORCID
classification cs.MScond-mat.stat-mechphysics.comp-ph
keywords noisegenmlcomplexcorrelatedenableslibrarymittag-lefflerpython
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Mittag-Leffler correlated noise (M-L noise) plays a crucial role in the dynamics of complex systems, yet the scientific community has lacked tools for its direct generation. Addressing this gap, our work introduces GenML, a Python library specifically designed for generating M-L noise. We detail the architecture and functionalities of GenML and its underlying algorithmic approach, which enables the precise simulation of M-L noise. The effectiveness of GenML is validated through quantitative analyses of autocorrelation functions and diffusion behaviors, showcasing its capability to accurately replicate theoretical noise properties. Our contribution with GenML enables the effective application of M-L noise data in numerical simulation and data-driven methods for describing complex systems, moving beyond mere theoretical modeling.

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