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20202023
most citedA Data-Driven Approach for Discovering Stochastic Dynamical Systems with Non-Gaussian Levy Noise

77 citations · 214 across the 11 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

math.DS2020★ 31 cited

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…

cs.CE2020★ 11 cited

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…

stat.ML2020★ 77 cited

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…

math.OC2020★ 28 cited

Enhanced energy harvesting using time-delayed feedback control from random rotational environment

Yanxia Zhang, Yanfei Jin, Yang Li

Motivated by improving performance of a bi-stable vibration energy harvester (VEH) from the viewpoint of vibration control, the time-delayed feedback control of displacement and ve…

math.ST2020★ 16 cited

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.…