Zobrazeno 1 - 10
of 398
pro vyhledávání: '"Chen, Chuchu"'
This paper investigates the structure preservation and convergence analysis of a class of fully discrete finite difference schemes for the stochastic heat equation driven by L\'evy space-time white noise. The novelty lies in the simultaneous preserva
Externí odkaz:
http://arxiv.org/abs/2409.14064
The superiority of stochastic symplectic methods over non-symplectic counterparts has been verified by plenty of numerical experiments, especially in capturing the asymptotic behaviour of the underlying solution process. How can one theoretically exp
Externí odkaz:
http://arxiv.org/abs/2404.14842
This paper investigates longtime behaviors of the $\theta$-Euler-Maruyama method for the stochastic functional differential equation with superlinearly growing coefficients. We focus on the longtime convergence analysis in mean-square sense and weak
Externí odkaz:
http://arxiv.org/abs/2404.08891
It is known from the monograph [1, Chapter 5] that the weak convergence analysis of numerical schemes for stochastic Maxwell equations is an unsolved problem. This paper aims to fill the gap by establishing the long-time weak convergence analysis of
Externí odkaz:
http://arxiv.org/abs/2403.09293
For stochastic wave equation, when the dissipative damping is a non-globally Lipschitz function of the velocity, there are few results on the long-time dynamics, in particular, the exponential ergodicity and strong law of large numbers, for the equat
Externí odkaz:
http://arxiv.org/abs/2402.01137
In order to give quantitative estimates for approximating the ergodic limit, we investigate probabilistic limit behaviors of time-averaging estimators of numerical discretizations for a class of time-homogeneous Markov processes, by studying the corr
Externí odkaz:
http://arxiv.org/abs/2310.08227
Robust multisensor fusion of multi-modal measurements such as IMUs, wheel encoders, cameras, LiDARs, and GPS holds great potential due to its innate ability to improve resilience to sensor failures and measurement outliers, thereby enabling robust au
Externí odkaz:
http://arxiv.org/abs/2309.15390
Autor:
Katragadda, Saimouli, Lee, Woosik, Peng, Yuxiang, Geneva, Patrick, Chen, Chuchu, Guo, Chao, Li, Mingyang, Huang, Guoquan
Achieving efficient and consistent localization a prior map remains challenging in robotics. Conventional keyframe-based approaches often suffers from sub-optimal viewpoints due to limited field of view (FOV) and/or constrained motion, thus degrading
Externí odkaz:
http://arxiv.org/abs/2309.09295
In this paper, we propose and analyze an adaptive time-stepping fully discrete scheme which possesses the optimal strong convergence order for the stochastic nonlinear Schr\"odinger equation with multiplicative noise. Based on the splitting skill and
Externí odkaz:
http://arxiv.org/abs/2212.01988
In this paper, we propose a novel kind of numerical approximations to inherit the ergodicity of stochastic Maxwell equations. The key to proving the ergodicity lies in the uniform regularity estimates of the numerical solutions with respect to time,
Externí odkaz:
http://arxiv.org/abs/2210.06092