Generation of short and long range temporal correlated noises
Aldo H. Romero, Jose M. Sancho
TL;DR
This paper introduces an algorithm for generating Gaussian random noises with specific temporal correlations, applicable to both short and long-range dependencies, demonstrated through Langevin dynamics examples.
Contribution
The paper presents a novel algorithm for generating Gaussian noises with customizable temporal correlations, expanding the tools available for stochastic process simulations.
Findings
Effective generation of correlated Gaussian noises demonstrated
Application to Langevin dynamics with diverse correlation ranges
Potential for improved modeling of correlated stochastic systems
Abstract
We present the implementation of an algorithm to generate Gaussian random noises with prescribed time correlations that can be either long or short ranged. Examples of Langevin dynamics with short and long range noises are presented and discussed.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.