A review that maps machine learning methods for characterizing anomalous diffusion and compares three strategies for representing diffusion trajectories.
GenML: A Python Library to Generate the Mittag-Leffler Correlated Noise
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Machine Learning Analysis of Anomalous Diffusion
A review that maps machine learning methods for characterizing anomalous diffusion and compares three strategies for representing diffusion trajectories.