GenML: A Python Library to Generate the Mittag-Leffler Correlated Noise
Authors: Xiang Qu, Hui Zhao, Wenjie Cai, Gongyi Wang, Zihan Huang
Summary: Mittag-Leffler correlated noise (M-L noise) performs an important function within the dynamics of complicated techniques, but the scientific group has lacked instruments for its direct technology. Addressing this hole, our work introduces GenML, a Python library particularly designed for producing M-L noise. We element the structure and functionalities of GenML and its underlying algorithmic strategy, which permits the exact simulation of M-L noise. The effectiveness of GenML is validated by quantitative analyses of autocorrelation capabilities and diffusion behaviors, showcasing its functionality to precisely replicate theoretical noise properties. Our contribution with GenML permits the efficient utility of M-L noise knowledge in numerical simulation and data-driven strategies for describing complicated techniques, shifting past mere theoretical modeling
