b'Jinqiao Duan'

DeepAI AI Chat

Featured Co-authors

Wei Wei
156 publications
Peng Zhang
141 publications
Xu Sun
140 publications
George Em Karniadakis
94 publications
Yang Li
74 publications
Liu Yang
73 publications
Tao Liu
58 publications
Ioannis G. Kevrekidis
42 publications
Jian Ren
42 publications
Jianyu Chen
38 publications
Yuhong Li
24 publications

research

∙ 09/16/2023

Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE

With the rapid development of AI technology, we have witnessed numerous ...

0 Pengbo Li, et al. ∙

research

∙ 09/07/2023

Early warning via transitions in latent stochastic dynamical systems

Early warnings for dynamical transitions in complex systems or high-dime...

0 Lingyu Feng, et al. ∙

research

∙ 08/24/2023

Privacy-Preserving Discretized Spiking Neural Networks

The rapid development of artificial intelligence has brought considerabl...

0 Pengbo Li, et al. ∙

research

∙ 07/12/2023

Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks

Stochastic Gumbel graph networks are proposed to learn high-dimensional ...

0 Jin Guo, et al. ∙

research

∙ 05/01/2023

Reservoir Computing with Error Correction: Long-term Behaviors of Stochastic Dynamical Systems

The prediction of stochastic dynamical systems and the capture of dynami...

2 Cheng Fang, et al. ∙

research

∙ 03/09/2023

Meta contrastive label correction for financial time series

Financial applications such as stock price forecasting, usually face an ...

0 Luxuan Yang, et al. ∙

research

∙ 07/22/2022

Numerical integration of stochastic contact Hamiltonian systems via stochastic Herglotz variational principle

In this work we construct a stochastic contact variational integrator an...

0 Qingyi Zhan, et al. ∙

research

∙ 05/09/2022

Auto-SDE: Learning effective reduced dynamics from data-driven stochastic dynamical systems

Multiscale stochastic dynamical systems have been widely adopted to scie...

20 Lingyu Feng, et al. ∙

research

∙ 03/31/2022

An Optimal Control Method to Compute the Most Likely Transition Path for Stochastic Dynamical Systems with Jumps

Many complex real world phenomena exhibit abrupt, intermittent or jumpin...

0 Wei Wei, et al. ∙

research

∙ 01/31/2022

An end-to-end deep learning approach for extracting stochastic dynamical systems with α-stable Lévy noise

Recently, extracting data-driven governing laws of dynamical systems thr...

3 Cheng Fang, et al. ∙

research

∙ 12/09/2021

Nonparametric inference of stochastic differential equations based on the relative entropy rate

The information detection of complex systems from data is currently unde...

0 Min Dai, et al. ∙

research

∙ 11/25/2021

Time Series Forecasting with Ensembled Stochastic Differential Equations Driven by Lévy Noise

With the fast development of modern deep learning techniques, the study ...

7 Luxuan Yang, et al. ∙

research

∙ 11/17/2021

Data-driven method to learn the most probable transition pathway and stochastic differential equations

Transition phenomena between metastable states play an important role in...

0 Jianyu Hu, et al. ∙

research

∙ 11/11/2021

The Most Likely Transition Path for a Class of Distribution-Dependent Stochastic Systems

Distribution-dependent stochastic dynamical systems arise widely in engi...

0 Wei Wei, et al. ∙

research

∙ 09/30/2021

Most Probable Transitions from Metastable to Oscillatory Regimes in a Carbon Cycle System

Global climate changes are related to the ocean's store of carbon. We st...

0 Wei Wei, et al. ∙

research

∙ 09/27/2021

Total value adjustment of Bermudan option valuation under pure jump Lévy fluctuations

During the COVID-19 pandemic, many institutions have announced that thei...

0 Gangnan Yuan, et al. ∙

research

∙ 07/29/2021

Learning the temporal evolution of multivariate densities via normalizing flows

In this work, we propose a method to learn probability distributions usi...

5 Yubin Lu, et al. ∙

research

∙ 03/28/2021

Variational inference of the drift function for stochastic differential equations driven by Lévy processes

In this paper, we consider the nonparametric estimation problem of the d...

0 Min Dai, et al. ∙

research

∙ 10/14/2020

Symplectic method for Hamiltonian stochastic differential equations with multiplicative Lévy noise in the sense of Marcus

A class of Hamiltonian stochastic differential equations with multiplica...

0 Qingyi Zhan, et al. ∙

research

∙ 08/24/2020

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

The Fokker-Planck (FP) equation governing the evolution of the probabili...

9 Xiaoli Chen, et al. ∙

research

∙ 06/28/2020

Symplectic Euler scheme for Hamiltonian stochastic differential equations driven by Levy noise

This paper proposes a general symplectic Euler scheme for a class of Ham...

0 Qingyi Zhan, et al. ∙

research

∙ 03/01/2020

Most Probable Dynamics of Stochastic Dynamical Systems with Exponentially Light Jump Fluctuations

The emergence of the exit events from a bounded domain containing a stab...

0 Yang Li, et al. ∙

research

∙ 02/18/2020

Identifying stochastic governing equations from data of the most probable transition trajectories

Extracting the governing stochastic differential equation model from elu...

0 Jian Ren, et al. ∙

research

∙ 01/06/2020

Maximum Likelihood Estimation of Stochastic Differential Equations with Random Effects Driven by Fractional Brownian Motion

Stochastic differential equations and stochastic dynamics are good model...

0 Min Dai, et al. ∙

research

∙ 10/21/2019

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

Physics-informed neural networks (PINNs) were recently proposed in [1] a...

0 Xiaoli Chen, et al. ∙

research

∙ 09/27/2018

A parameter estimator based on Smoluchowski-Kramers approximation

We devise a simplified parameter estimator for a second order stochastic...

0 Ziying He, et al. ∙

research

∙ 03/10/2013

State estimation under non-Gaussian Levy noise: A modified Kalman filtering method

The Kalman filter is extensively used for state estimation for linear sy...

0 Xu Sun, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world