Core Concepts
GenML introduces a Python library to generate Mittag-Leffler correlated noise, addressing the lack of tools for its direct generation. The software enables precise simulation of M-L noise, validating its effectiveness through quantitative analyses.
Abstract
GenML is a Python library developed to generate Mittag-Leffler correlated noise, crucial in understanding complex systems. The software fills a gap in direct M-L noise generation tools, facilitating its application in simulations and data-driven methods across various scientific fields.
The content discusses the importance of non-white noise like M-L noise in revealing complex system dynamics. It highlights the versatility of M-L noise in modeling various phenomena and showcases examples where it has been applied effectively.
The article details the architecture and functionalities of GenML, providing insights into how the software generates M-L noise sequences accurately. It also presents illustrative examples and comparisons between calculated and theoretical values to validate GenML's effectiveness.
Furthermore, the impact of GenML on research and applications is discussed, emphasizing how it enhances the understanding of complex systems by bridging theoretical models with practical simulations. The conclusion highlights GenML as a reliable tool for researchers and developers, paving the way for future discoveries.
Stats
Mittag-Leffler correlated noise (M-L noise) plays a crucial role in complex systems dynamics.
GenML is designed to generate M-L noise accurately through quantitative analyses.
The software architecture includes steps for selecting optimal sequence length and generating M-L noise sequences.
APIs provided by GenML allow users to calculate autocorrelation function values and compare them with theoretical values.
Examples are given to demonstrate generated M-L noise sequences using different input parameters.