Detecting the maximum likelihood transition path from data of stochastic dynamical systems

DOI10.1063/5.0012858zbMath1457.37072arXiv2004.11513OpenAlexW3102130035WikidataQ103824589 ScholiaQ103824589MaRDI QIDQ5140892

Yayun Zheng, Yu-Bin Lu, Ting Gao, Min Dai, Jin-qiao Duan

Publication date: 17 December 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.11513

zbMATH Keywords

stochastic dynamical systemdata-driven methodscomplex dynamical systemsmultiplicative Gaussian noiseKramers-Moyal formula

Mathematics Subject Classification ID

White noise theory (60H40) Generation, random and stochastic difference and differential equations (37H10) Time series analysis of dynamical systems (37M10) Simulation of dynamical systems (37M05)

Related Items (6)

Weak SINDy for partial differential equations ⋮ A machine learning method for computing quasi-potential of stochastic dynamical systems ⋮ Stochastic dynamics and data science ⋮ Nonparametric inference of stochastic differential equations based on the relative entropy rate ⋮ Extracting governing laws from sample path data of non-Gaussian stochastic dynamical systems ⋮ Most probable trajectories in a two-dimensional tumor-immune system under stochastic perturbation

Uses Software

• GitHub

Cites Work

• Unnamed Item
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• A data-driven approximation of the koopman operator: extending dynamic mode decomposition
• Theory and applications of stochastic processes. An analytical approach
• Most probable transition pathways and maximal likely trajectories in a genetic regulatory system
• Governing equations for probability densities of stochastic differential equations with discrete time delays
• Dynamic mode decomposition of numerical and experimental data
• Dynamical inference for transitions in stochastic systems withα-stable Lévy noise
• Variational estimation of the drift for stochastic differential equations from the empirical density
• Robust data-driven discovery of governing physical laws with error bars
• Reconstruction of normal forms by learning informed observation geometries from data
• Sparse dynamics for partial differential equations
• The maximum likelihood climate change for global warming under the influence of greenhouse effect and Lévy noise
• Discovering mean residence time and escape probability from data of stochastic dynamical systems
• Data-Driven Identification of Parametric Partial Differential Equations

This page was built for publication: Detecting the maximum likelihood transition path from data of stochastic dynamical systems