4 papers
A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systems
Yang Li, Jinqiao Duan, Xianbin Liu
The emergence of transition phenomena between metastable states induced by noise plays a fundamental role in a broad range of nonlinear systems. The computation of the most probabl…
Extracting Non-Gaussian Governing Laws from Data on Mean Exit Time
Yanxia Zhang, Jinqiao Duan, Yanfei Jin +1
Motivated by the existing difficulties in establishing mathematical models and in observing the system state time series for some complex systems, especially for those driven by no…
A Data-Driven Approach for Discovering Stochastic Dynamical Systems with Non-Gaussian Levy Noise
Yang Li, Jinqiao Duan
With the rapid increase of valuable observational, experimental and simulating data for complex systems, great efforts are being devoted to discovering governing laws underlying th…
Most Probable Dynamics of Stochastic Dynamical Systems with Exponentially Light Jump Fluctuations
Yang Li, Jinqiao Duan, Xianbin Liu +1
The emergence of the exit events from a bounded domain containing a stable fixed point induced by non-Gaussian Lévy fluctuations plays a pivotal role in practical physical systems.…