182 citations · 1.5k across the 227 of their papers we have counts for
6 papers · 1 filter
Early warning indicators via latent stochastic dynamical systems
Lingyu Feng, Ting Gao, Wang Xiao +1
Detecting early warning indicators for abrupt dynamical transitions in complex systems or high-dimensional observation data is essential in many real-world applications, such as br…
Learning effective dynamics from data-driven stochastic systems
Lingyu Feng, Ting Gao, Min Dai +1
Multiscale stochastic dynamical systems have been widely adopted to a variety of scientific and engineering problems due to their capability of depicting complex phenomena in many…
An end-to-end deep learning approach for extracting stochastic dynamical systems with -stable Lévy noise
Cheng Fang, Yubin Lu, Ting Gao +1
Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields. Moreover, a growing amount of…
Extracting stochastic dynamical systems with -stable Lévy noise from data
Yang Li, Yubin Lu, Shengyuan Xu +1
With the rapid increase of valuable observational, experimental and simulated data for complex systems, much efforts have been devoted to identifying governing laws underlying the…
Learning the temporal evolution of multivariate densities via normalizing flows
Yubin Lu, Romit Maulik, Ting Gao +3
In this work, we propose a method to learn multivariate probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporall…
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…