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09/16/2023
Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE
With the rapid development of AI technology, we have witnessed numerous ...
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09/07/2023
Early warning via transitions in latent stochastic dynamical systems
Early warnings for dynamical transitions in complex systems or high-dime...
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08/24/2023
Privacy-Preserving Discretized Spiking Neural Networks
The rapid development of artificial intelligence has brought considerabl...
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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 ...
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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...
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03/09/2023
Meta contrastive label correction for financial time series
Financial applications such as stock price forecasting, usually face an ...
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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...
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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...
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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...
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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...
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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...
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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 ...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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02/18/2020
Identifying stochastic governing equations from data of the most probable transition trajectories
Extracting the governing stochastic differential equation model from elu...
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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...
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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...
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09/27/2018
A parameter estimator based on Smoluchowski-Kramers approximation
We devise a simplified parameter estimator for a second order stochastic...
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03/10/2013