In this article, we investigate the asymptotic behavior of solutions to the following stochastic nonclassical diffusion equations driven by additive noise and linear memory:
where is a bounded domain in , the initial data , is a real valued function of , , , and is the generalized time derivative of an infinite dimensional wiener process defined on a probability space , where , is the σ-algebra of Borel sets induced by the compact topology of , is a corresponding wiener measure on for which the canonical wiener process satisfies that both and are usual one dimensional Brownian motions. We may identify with , that is, for all .
To consider system (1.1), we assume that the memory kernel satisfies
and there exists a positive constant such that the function satisfies
And suppose that the nonlinearity satisfies as follows:
, and for every fixed , satisfying
where and l are positive constants, and q is a conjugate exponent of p.
In addition, we assume that for and , for ; , for .
We assume that the time-dependent external force term satisfies a condition
and for some constant to be specified later.
Equation (1.1) has its physical background in the mathematical description of viscoelastic materials. It’s well known that the viscoelastic material exhibit natural damping, which according to the special property of these materials to retain a memory of their past history. And from the materials point of view, the property of memory comes from the memory kernel , which decays to zero with exponential rate. Many authors have constructed the mathematical model by some concrete examples, see  -  . In  the authors considered the nonclassical diffusion equation with hereditary memory on a 3D bounded domains for a very general class of memory kernels ; setting the problem both in the classical past history framework and in the more recent minimal state one, the related solution semigroups are shown to possess finite-dimensional regular exponential attractors. Equation (1.1) is a special case of the nonclassical diffusion equation used in fluid mechanics, solid mechanics, and heat conduction theory (see    ). In  Aifantis, Urbana and Illinois discussed some basic mathematical results concerning certain new types of some equations, and in particular results showing how solutions of some equations can be expressed at in terms of solutions of the heat equation, also discussed diffusion in general viscoelastic and plastic solids. In  Kuttler and Aifantis presented a class of diffusion models that arise in certain nonclassical physical situations and discuss existence and uniqueness of the resulting evolution equations.
The long-time behavior of Equation (1.1) without white additive noise and has been considered by many researchers; on a bounded domain see, e.g.      and the references therein. In  the authors proved the existence and the regularity of time-dependent global attractors for a class of nonclassical reaction-diffusion equations when the forcing term and the nonlinear function satisfies the critical exponent growth. In  Sun and Yang proved the existence of a global attractor for the autonomous case provided that the nonlinearity is critical and . The researchers in  obtained the Pullback attractors for the nonclassical diffusion equations with the variable delay on a bounded domain, where the nonlinearity is at most two orders growth. As far as the unbounded case for the system (1.1) the long-time behavior of solutions is concerned, most recently, by the tail estimate technique and some omega-limit compactness argument, for more details (see      ). In  Ma studied the existence of global attractors for nonclassical diffusion equations with the arbitrary order polynomial growth conditions. By a similar technique, Zhang in  obtained the Pullback attractors for the non-autonomous case in , where the growth order of the nonlinearity is assumed to be controlled by the space dimension N, such that the Sobolev embedding is continuous. However, it is regretted that some terms in the proof of  Lemma 3.4 are lost. Anh et al.  established the existence of pullback attractor in the space , where the nonlinearity satisfied an arbitrary polynomial growth, but some additional assumptions on the primitive function of the nonlinearity were required. And the case of with additive noise on a bounded domain, Cheng used the decomposition method of the solution operator to consider the stochastic nonclassical diffusion equation with fading memory. For the case of , Zhao studied the dynamics of stochastic nonclassical diffusion equations on unbounded domains perturbed by a ò-random term “intension of noise”. (For more details see      ).
To our best knowledge, Equation (1.1) on a bounded domain in the weak topological space and the time-dependent forcing term has not been considered by any predecessors.
The article is organized as follows. In Section two, we recall the fundamental results related to some basic function spaces and the existence of random attractors. In Section three, firstly, we define a continuous random dynamical system to proving the existence and uniqueness of the solution, then prove the existence of a closed random absorbing set and establish the asymptotic compactness of the random dynamical system finally prove the existence of D-random attractor.
In this section, we recall some basic concepts and results related to function spaces and the existence of random attractors of the RDSs. For a comprehensive exposition on this topic, there is a large volume of literature, see     -  .
Let , with the domain , and fractional
power space , , the , is the inner product and norm, respectively. For convenience, we use , the norm , and , .
Similar to  , for the memory kernel , we denote the Hilbert space of function , endowed with the inner product and norm respectively,
Define the space
with the inner product
and the norm
We also introduce the family of Hilbert space , and endow norm
In the following of this article, we denote . See    for more details.
Let , f is the Borel σ-algebra on , and is the corresponding Wiener measure. Define
Then is the measurable map and is the identity on , for all . That is, is called a metric dynamical system.
Definition 2.1. is called a metric dynamical system if is -measurable, is the identity on , for all and for all .
Definition 2.2. A continuous random dynamical system (RDS) on X over a metric dynamical system is a mapping
which is -measurable and satisfied, for P-a.e. ,
1) is the identity on X;
2) for all ;
3) is continuous for all .
Definition 2.3. A random bounded set is a family of nonempty subsets of X is called tempered with respect to if for P-a.e. , for all ,
Definition 2.4. Let be the collection of all tempered random sets in X. A set is called a random absorbing set for RDS in , if for every and P-a.e. , there exists such that for all ,
Definition 2.5. Let be the collection of all tempered random subsets of X. Then is said to be asymptotically compact in X if for P-a.e. , the sequence has a convergent subsequence in X whenever
, and with .
Definition 2.6. (See  ,  ,  ) Let be the collection of all tempered random subsets of X and . Then is called a D-random attractor for if the following conditions are satisfied, for P-a.e. ,
1) is compact, and is measurable for every ;
2) is invariant, that is, , ;
3) attracts every set in , that is, for every
where d is the Hausdorff semi-metric given by
for any and .
Theorem 2.1. Let be a continuous random dynamical system with state space X over . If there is a closed random absorbing set of and is asymptotically compact in X, then is a random attractor of , where
Moreover, is the unique random attractor of .
As mentioned in  , we can define a new variable to reflect the memory kernel of (1.1)
Therefore, we can rewrite (1.1) as follows.
where satisfies that there exist two positive constant C and k, such that
Lemma 2.1. (   ) Assume that is a nonnegative function, and there exists , such that , then holds for all . Moreover, for three Banach space and , and are reflexive and
where, the embedding is compact. Let satisfy
1) K in ;
2) , a.s. For some .
Then K is relatively compact in .
3. The Random Attractor
In this section, we prove that the stochastic nonclassical diffusion problem (2.4) has a D-random attractor. First, We convert system (2.4) with a random perturbation term and linear memory into a deterministic one with a random parameter . For this purpose, we introduce the Ornstein-Uhlenbeck process taking the form
where is one dimensional Wiener process defined in the introduction. Furthermore, satisfies the stochastic differential equations
It is known that there exists a -invariant set of full P measure such that is continuous for every , and the random variable is tempered, see, e.g.,  It is easy to show that
where is the Laplacian with domain . Using the change of variable , satisfies the equation (which depends on the random parameter )
By the Galerkin method as in  , under assumptions (1.2)-(1.8), for P-a.e. , and for all , problem (3.3) has a unique solution in , satisfying .
Throughout this article, we always write
If u is the solution of problem (1.1) in some sense, we can define a continuous dynamical system
In order to prove the asymptotic compactness and the existence of global attractor, we give the following results.
Lemma 3.1. (  ) Set , . Let the memory kernel satisfy (1.3), then for any , , there exists a constant , such that
We first show that the random dynamical system has a closed random absorbing set in , and then prove that is asymptotically compact.
Lemma 3.2. Assume that and (1.2)-(1.8) hold. Let . Then for P-a.e. , there is a positive random function and a constant such that for all ,
the solution of (3.3) has the following uniform estimate
Proof. Taking the inner product of the first equation of (3.3) with , we have
From (2.2) and (2.3), we obtain
Hence, we can rewrite (3.8) as follows
By Young inequality and Lemma 3.1, we get
From the first term on the right hand side of (3.8) , First we estimate . By (1.4)-(1.5) and using a similar arguments as (4.2) in  , we have
By using (1.6)-(1.7), we arrive at
By the young inequality, and using assumption (1.6), we see that
where . Then, it follows from (3.13)-(3.15) that
On the other hand, we have
From the last term of (3.8), we obtain
Then, we substituting (3.11), (3.12) and (3.16)-(3.18) into (3.10) we conclude that
then we have
Then (3.19)-(3.20), it implies
According to Grnowall's Lemma, we obtain
Substituting by , then from (3.22), we have that
Recalling that is tempered such that
Note that is the tempered, and , , we can choose
Then is the tempered since has at most linear growth rate at infinity, now the proof is completed.
To prove the asymptotic compactness of the solution, we decompose the solution of (3.3) as follows   :
where satisfy the following problems, respectively
here the nonlinearity are satisfies (1.4)-(1.7). The drifting term and the forcing term satisfies a condition as in (1.8), , for any , such that
Set , we find that
Let , where satisfies (3.26), and , is the solution of (3.27). Then for and we have that
The same of the problem (3.3), we also have the corresponding existence and uniqueness of solutions for (3.30) and (3.31). For the convenience, we obtain the solution operators of (3.30) and (3.31) by and respectively. Then, for every , we get
Next, we give some Lemmas to prove the asymptotic compactness.
Lemma 3.3. Assume that the condition on hold. Let . Then for P-a.e. , there is a constant , , if
then for all , the solution of (3.30) satisfies the following uniform estimate
where the positive random function is defined in Lemma 3.2.
Proof. From (3.10) we substituting by , respectively. Similar to the proof the Lemma 3.2, we compute
Since , are tempered, we can choose , , such that (3.32) is satisfied.
Lemma 3.4. Assume that the condition on hold. Let . Then for P-a.e. , there is a positive random function and
such that for every given , the solution of (3.31) has the following uniform estimates
Proof. Multiplying (3.31) by and integrating over , we can get
From (2.2) and (3.31), we obtain
By and the mean value theorem, we have
Using the embedding theorem, we have
where we have used inequality , so and the embedding theorem
Thanks to Lemma 3.1, the property of the solution of (3.3) and (3.26), and (3.36)-(3.40), we conclude that
Thus, for every given , we get
where is a random function.
The proof is complete.
Since and (3.31), it follows
for more information on , see  , we have
Lemma 3.5. Let is a projection operator, setting , is a random bounded absorbing set from Lemma 3.4, is the solution operator of (3.31), and under the assumption of Lemma 3.4, there is a positive random function depend on T, such that
1) is bounded in ;
Proof. By the random translation, (3.44) and Lemma 3.4, we can prove this Lemma.
Therefore, Lemma 2.1 implies that is relatively compact in . And use the compact embedding , we have that
Lemma 3.6. Let be the corresponding solution operator of (3.31), and the assumption of Lemma 3.4 and 3.5 hold, then for any , is relatively compact in .
Now we are on a position to prove the existence of a random attractor for the stochastic nonclassical diffusion equation with linear memory and additive white noise.
Theorem 3.1. Let be the solution operator of equation (3.3), and the conditions of the lemma 3.6 hold, then the random dynamical system has a unique random attractor in .
Proof. Notice that has a closed absorbing set by Lemma 3.2, and is relatively compact in by Lemma 3.3 and Lemma 3.6. Hence the existence of a unique D-random attractor follows from Theorem 2.1 immediately.
This work was supported by the NSFC (11561064), and NWNU-LKQN-14-6.
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