Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 598, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physa.2022.127386
Keywords
Stochastic modeling; Statistical inference; Turbulence; Kinetic theory; Intermittency
Categories
Funding
- National Science Foundation [1622488]
- Hanse-Wissenschaftskolleg (Delmenhorst)
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1622488] Funding Source: National Science Foundation
Ask authors/readers for more resources
The dynamics of complex systems can be successfully modeled as stochastic diffusion processes, even when the real dynamics are not strictly diffusive. Current nonparametric estimation methods may lead to inconsistent results with the probability distribution of the system. We propose a novel estimation technique that provides drift and diffusion consistent with the observed probability density functions for turbulent flow and molecular motion in gas.
Dynamics of complex systems can often be successfully modeled as a stochastic diffusion process, even if the real dynamics are not strictly diffusive. We show that for such systems current methods for nonparametric estimation of the drift and diffusion terms may lead to results that are inconsistent with the probability distribution of the system. We present a novel estimation technique that for the two systems studied, turbulent flow and molecular motion in gas, produces drift and diffusion consistent with the observed probability density functions. The presented method is applicable to systems with smooth real dynamics. (C) 2022 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available