Dynamical inference for transitions in stochastic systems with α-stable Lévy noise

Ting Gao; Jinqiao Duan; Xingye Kan; Zhuan Cheng

doi:10.1088/1751-8113/49/29/294002

Dynamical inference for transitions in stochastic systems with α-stable Lévy noise

Ting Gao, Jinqiao Duan, Xingye Kan, Zhuan Cheng

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

A goal of data assimilation is to infer stochastic dynamical behaviors with available observations. We consider transition phenomena between metastable states for a stochastic system with (non-Gaussian) α;-stable Lévy noise. With either discrete time or continuous time observations, we infer such transitions between metastable states by computing the corresponding non-local Zakai equation (and its discrete time counterpart) and examining the most probable orbits for the state system. Examples are presented to demonstrate this approach.

Original language	English (US)
Article number	294002
Journal	Journal of Physics A: Mathematical and Theoretical
Volume	49
Issue number	29
DOIs	https://doi.org/10.1088/1751-8113/49/29/294002
State	Published - Jun 10 2016
Externally published	Yes

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© 2016 IOP Publishing Ltd.

Keywords

mean exit time with observation
most probable orbits
non-local Laplace operator
non-local Zakai equation
transitions between metastable states

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10.1088/1751-8113/49/29/294002

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title = "Dynamical inference for transitions in stochastic systems with α-stable L{\'e}vy noise",

abstract = "A goal of data assimilation is to infer stochastic dynamical behaviors with available observations. We consider transition phenomena between metastable states for a stochastic system with (non-Gaussian) α;-stable L{\'e}vy noise. With either discrete time or continuous time observations, we infer such transitions between metastable states by computing the corresponding non-local Zakai equation (and its discrete time counterpart) and examining the most probable orbits for the state system. Examples are presented to demonstrate this approach.",

keywords = "mean exit time with observation, most probable orbits, non-local Laplace operator, non-local Zakai equation, transitions between metastable states",

author = "Ting Gao and Jinqiao Duan and Xingye Kan and Zhuan Cheng",

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T1 - Dynamical inference for transitions in stochastic systems with α-stable Lévy noise

AU - Gao, Ting

AU - Duan, Jinqiao

AU - Kan, Xingye

AU - Cheng, Zhuan

PY - 2016/6/10

Y1 - 2016/6/10

N2 - A goal of data assimilation is to infer stochastic dynamical behaviors with available observations. We consider transition phenomena between metastable states for a stochastic system with (non-Gaussian) α;-stable Lévy noise. With either discrete time or continuous time observations, we infer such transitions between metastable states by computing the corresponding non-local Zakai equation (and its discrete time counterpart) and examining the most probable orbits for the state system. Examples are presented to demonstrate this approach.

AB - A goal of data assimilation is to infer stochastic dynamical behaviors with available observations. We consider transition phenomena between metastable states for a stochastic system with (non-Gaussian) α;-stable Lévy noise. With either discrete time or continuous time observations, we infer such transitions between metastable states by computing the corresponding non-local Zakai equation (and its discrete time counterpart) and examining the most probable orbits for the state system. Examples are presented to demonstrate this approach.

KW - mean exit time with observation

KW - most probable orbits

KW - non-local Laplace operator

KW - non-local Zakai equation

KW - transitions between metastable states

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DO - 10.1088/1751-8113/49/29/294002

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AN - SCOPUS:84976351261

SN - 1751-8113

VL - 49

JO - Journal of Physics A: Mathematical and Theoretical

JF - Journal of Physics A: Mathematical and Theoretical

IS - 29

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ER -

Dynamical inference for transitions in stochastic systems with α-stable Lévy noise

Abstract

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