Results 81 to 90 of about 206 (175)
In this paper we investigate the stochastic retarded reaction-diffusion equations with multiplicative white noise on unbounded domain ℝn (n ≥ 2). We first transform the retarded reaction-diffusion equations into the deterministic reaction-diffusion ...
Jia Xiaoyao, Ding Xiaoquan, Gao Juanjuan
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Pullback attractors for lattice FitzHugh-Nagumo systems with fast-varying delays
We investigate the dynamical behavior of lattice FitzHugh-Nagumo equation with fast-varying delays and obtain the existence and uniqueness of pullback attractor for the equation. Generally, studying the attractors of a time-varying delay equation require
WANG Xue-Min
doaj
Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19. [PDF]
Rakhshan SA, Nejad MS, Zaj M, Ghane FH.
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In this manuscript previous results [Nonlinearity 25(2012), 905–930] are extended to a non-autonomous 3D Navier–Stokes–Voigt model in which a forcing term contains memory effects.
Julia García-Luengo, Pedro Marín-Rubio
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Tipping points induced by parameter drift in an excitable ocean model. [PDF]
Pierini S, Ghil M.
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Pullback attractor of the 2D non-autonomous magneto-micropolar fluid equations
The purpose of this article is to establish the existence of the pullback attractors for the non-autonomous magneto-micropolar fluid equations in 2D bounded domains.
Zhou Gang, Gao Rui, Tian Congyang
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Stochastic Chaos and Markov Blankets. [PDF]
Friston K +4 more
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Pullback attractors for non-autonomous parabolic equations involving Grushin operators
Using the asymptotic a priori estimate method, we prove the existence of pullback attractors for a non-autonomous semilinear degenerate parabolic equation involving the Grushin operator in a bounded domain.
Cung The Anh
doaj
How can contemporary climate research help understand epidemic dynamics? Ensemble approach and snapshot attractors. [PDF]
Kovács T.
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On the Dimension of Pullback Attractors in Recurrent Neural Networks
Recurrent neural networks trained via the reservoir computing paradigm have demonstrated remarkable success in learning and reconstructing attractors from chaotic systems, often replicating quantities such as Lyapunov exponents and fractal dimensions.
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