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

Abstract

In recent years, the discovery of complex dynamic systems in various fields through data-driven methods has attracted widespread attention. This method has played the role of data and has become an advantageous tool for us to study complex phenomena. In this work, we propose a framework for detecting the dynamic behavior, such as the maximum likelihood transition path, of stochastic dynamic systems from data. For the stochastic dynamic system, we need to use the Kramers-Moyal formula to convert it into a deterministic form for processing, then use the extended SINDy method to obtain the parameters of stochastic dynamic systems, and finally calculate the maximum likelihood transition path. We give two examples of stochastic dynamical systems driven by additive and multiplicative Gaussian noise, and demonstrate the validity of the method by reproducing the known dynamical system behavior.

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Cited by in corpus (7)

• Weak SINDy For Partial Differential Equations
• Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems
• Lévy-noise versus Gaussian-noise-induced Transitions in the Ghil-Sellers Energy Balance Model
• Learning the temporal evolution of multivariate densities via normalizing flows
• An end-to-end deep learning approach for extracting stochastic dynamical systems with $\alpha$-stable Lévy noise
• Extracting Stochastic Governing Laws by Nonlocal Kramers-Moyal Formulas
• Early warning indicators via latent stochastic dynamical systems