Results 81 to 90 of about 182 (102)

Comprehending symmetry in epidemiology: A review of analytical methods and insights from models of COVID-19, Ebola, Dengue, and Monkeypox. [PDF]

Medicine (Baltimore)
Shanmugam SN, Byeon H.
europepmc   +1 more source

CT synthesis with deep learning for MR-only radiotherapy planning: a review. [PDF]

Biomed Eng Lett
Roh J, Ryu D, Lee J.
europepmc   +1 more source

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General decay stability of backward Euler–Maruyama method for nonlinear stochastic integro-differential equations

Applied Mathematics Letters, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Linna Liu, Feiqi Deng, Boyang Qu 0001, Jianyin Fang   +3 more
openaire   +4 more sources

Stability in the small moment sense of the backward Euler–Maruyama method for stochastic differential equations with super-linear coefficients

Applied Mathematics Letters, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiaotong Li, Wei Liu 0039, Yudong Wang, Ruoxue Wu   +3 more
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Backward Euler-Maruyama method applied to nonlinear hybrid stochastic differential equations with time-variable delay

Science China Mathematics, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Chengjian, Xie, Ying
openaire   +4 more sources

Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks

SPIE Proceedings, 2015
Stability analysis of various neural networks have been successfully applied in many fields such as parallel computing and pattern recognition. This paper is concerned with a class of stochastic Markovian jump neural networks. The general mean-square stability of Backward Euler-Maruyama method for stochastic Markovian jump neural networks is discussed.
Hua Yang, Jianguo Liu, Yi Liu, Xiaofeng Yue   +3 more
openaire   +1 more source

Mean‐square stability of the backward Euler–Maruyama method for neutral stochastic delay differential equations with jumps

Mathematical Methods in the Applied Sciences, 2016
This paper is mainly considered whether the mean‐square stability of neutral stochastic delay differential equations (NSDDEs) with jumps is shared with that of the backward Euler–Maruyama method. Under the one‐sided Lipschitz condition and the linear growth condition, the trivial solution of NSDDEs with jumps is proved to be mean‐square stable by using
Mo, Haoyi, Zhao, Xueyan, Deng, Feiqi
openaire   +1 more source

Parameter-Related Strong Convergence Rate of the Backward Euler–Maruyama Method for Time-Changed Stochastic Differential Equations

Fluctuation and Noise Letters
The strong convergence of the backward Euler–Maruyama method for time-changed stochastic differential equations with additive noise is investigated. This work focuses on the detailed study of the relationship between the strong convergence rate and the parameter, [Formula: see text], of the inverse subordinator. The strong convergence rate of [Formula:
Wei Liu, Meng Wang, Ruchun Zuo
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Strong convergence rates for backward Euler–Maruyama method for non-linear dissipative-type stochastic differential equations with super-linear diffusion coefficients

Stochastics, 2013
Xuerong Mao, Lukasz Szpruch
exaly  

Mean‐square stability of the backward Euler–Maruyama method for neutral stochastic delay differential equations with jumps

Mathematical Methods in the Applied Sciences, 2017
Haoyi Mo, Xueyan Zhao, Feiqi Deng
exaly