activity
20172020
most citedSolving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

11 citations · 22 across the 4 of their papers we have counts for

collaborators

5 papers

physics.comp-ph202011 cited

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

Xiaoli Chen, Liu Yang, Jinqiao Duan +1

The Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficient…

physics.comp-ph201910 cited

Learning and Meta-Learning of Stochastic Advection-Diffusion-Reaction Systems from Sparse Measurements

Xiaoli Chen, Jinqiao Duan, George Em Karniadakis

Physics-informed neural networks (PINNs) were recently proposed in [1] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the so…

math.DS2018

Most probable dynamics of a genetic regulatory network under stable Lévy noise

Xiaoli Chen, Fengyan Wu, Jinqiao Duan +2

Numerous studies have demonstrated the important role of noise in the dynamical behaviour of a complex system. The most probable trajectories of nonlinear systems under the influen…

math.DS20181 cited

Data assimilation and parameter estimation for a multiscale stochastic system with alpha-stable Levy noise

Yanjie Zhang, Zhuan Cheng, Xinyong Zhang +3

This work is about low dimensional reduction for a slow-fast data assimilation system with non-Gaussian stable Lévy noise via stochastic averaging. When the observations are on…

q-bio.MN2017

Lévy noise-induced transitions in gene regulatory networks

Fengyan Wu, Xiaoli Chen, Yayun Zheng +3

Important effects of noise on a one-dimensional gene expression model involving a single gene have recently been discussed. However, few works have been devoted to the transition i…