DCDS
Non--autonomous and random attractors for delay random semilinear equations without uniqueness
Tomás Caraballo M. J. Garrido-Atienza B. Schmalfuss José Valero
We first prove the existence and uniqueness of pullback and random attractors for abstract multi-valued non-autonomous and random dynamical systems. The standard assumption of compactness of these systems can be replaced by the assumption of asymptotic compactness. Then, we apply the abstract theory to handle a random reaction-diffusion equation with memory or delay terms which can be considered on the complete past defined by $\mathbb{R}^{-}$. In particular, we do not assume the uniqueness of solutions of these equations.
keywords: multi-valued non-autonomous and random dynamical systems pullback and random attractors delay differential equations. Cocycles
DCDS
Existence of exponentially attracting stationary solutions for delay evolution equations
Tomás Caraballo M. J. Garrido-Atienza B. Schmalfuss
We consider the exponential stability of semilinear stochastic evolution equations with delays when zero is not a solution for these equations. We prove the existence of a non-trivial stationary solution exponentially stable, for which we use a general random fixed point theorem for general cocycles. We also construct stationary solutions with the stronger property of attracting bounded sets uniformly, by means of the theory of random dynamical systems and their conjugation properties.
keywords: stationary solutions exponential stability. random dynamical systems delay equations cocycles
CPAA
Dynamics of some stochastic chemostat models with multiplicative noise
T. Caraballo M. J. Garrido-Atienza J. López-de-la-Cruz

In this paper we study two stochastic chemostat models, with and without wall growth, driven by a white noise. Specifically, we analyze the existence and uniqueness of solutions for these models, as well as the existence of the random attractor associated to the random dynamical system generated by the solution. The analysis will be carried out by means of the well-known Ornstein-Uhlenbeck process, that allows us to transform our stochastic chemostat models into random ones.

keywords: Chemostat stochastic differential equations multiplicative noise random dynamical systems random attractors

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