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Volume 2018, Article ID 7402764, 17 pages
Research Article

Moderate Deviations for Stochastic Fractional Heat Equation Driven by Fractional Noise

1College of Science, Bengbu University, 1866 Caoshan Rd., Bengbu 233030, China
2School of Information Science and Technology, East China Normal University, No. 500, Dong-Chuan Road, Shanghai 200241, China
3American University of Sharjah, P.O. Box 26666, Sharjah, UAE

Correspondence should be addressed to Xichao Sun; moc.621@626oahcixnus

Received 28 January 2018; Revised 6 April 2018; Accepted 2 May 2018; Published 10 July 2018

Academic Editor: Maricel Agop

Copyright © 2018 Xichao Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We consider a class of stochastic fractional heat equations driven by fractional noises. A central limit theorem is given, and a moderate deviation principle is established.

1. Introduction

Since the work of Freidlin and Wentzell [1], the large deviation principle (LDP) has been extensively developed for small noise systems and other types of models (such as interacting particle systems) (see [27]). Cardon-Weber [2] proved a LDP for a Burgers-type SPDE driven by white noise. Marquez-Carreras and Sarra [3] proved a LDP for a stochastic heat equation with spatially correlated noise, and Mellali and Mellouk [4] extended Marquez-Carreras and Sarra’s [3] to a fractional operator. Jiang et al. [5] proved a LDP for a fourth-order stochastic heat equation driven by fractional noise. Budhiraja et al. [6] studied large deviation properties of systems of weakly interacting particles. Budhiraja et al. [7] proved a large deviation for Brownian particle systems with killing.

Similar to the large deviation, the moderate deviation problems also come from the theory of statistical inference. Using the moderate deviation principle (MDP), we can get the rate of convergence and an important method to construct asymptotic confidence intervals, for example, Liming [8], Guillin and Liptser [9], Cattani and Ciancio [10], and other references therein. There are also many works about MDP about stochastic (partial) differential equations; some surveys and literatures could be found in Budhiraja et al. [11], Wang and Zhang [12], Li et al. [13], Yang and Jiang [14], and the references therein. On the other hand, fractional equations have attracted many physicists and mathematicians due to various applications in risk management, image analysis, and statistical mechanics (see Droniou and Imbert [15], Bakhoum and Toma [16], Levy and Pinchas [17], Mardani et al. [18], Niculescu et al. [19], Paun [20], and Pinchas [21] for a survey of applications). Stochastic partial differential equations involving a fractional Laplacian operator have been studied by many authors; see Mueller [22], Wu [23], Liu et al. [24], Wu [25], and the references therein.

Motived above, we investigated the moderate deviations about the stochastic fractional heat equation with fractional noise as follows: where , , is the fractional Laplacian operator which is defined in Appendix, and denotes a fractional noise which is fractional in time and white in space with Hurst parameter ; that is, is a mean zero Gaussian random field on with covariance.

Assume that the coefficients satisfy the following.

Assumption 1. Function is Lipschitz; that is, there exist an satisfying

Under the conditions of Assumption 1, (1) possesses a unique solution in the sense of Walsh [26] as follows:

As the parameter , the solution of (1) will tend to which is the solution to the following equation:

This paper mainly devotes to investigate the deviations of from the deterministic solution , as , that is, the asymptotic behavior of the trajectories. where is the same deviation scale that strongly influences the asymptotic behavior of .

If , we are in the domain of large deviation estimate, which can be proved similarly to Jiang et al. [5].

The case provides the central limit theorem. As , we will prove that converges to a random field in this paper.

To fill the gap between scale and scale , we mainly devote to the moderate deviation when the scale satisfies the following:

This paper is organized as follows. In Section 2, the definition of the fractional noise is given. In Section 3, the main result is given and proved. In Appendix, some results about the Green kernel are given.

2. Fractional Noise

Let , and is a centered Gaussian family of random variables with the covariance satisfying with and covariance kernel , where denotes the Lebesgue measure of the set and denotes the class of Borel sets in .

We denote as the set of step functions on . Let be the Hilbert space defined as the closure of with respect to the scalar product.

According to Nualart and Ouknine [27], the mapping can be extended to an isometry between and the Gaussian space associated with and denoted by

Define the linear operator by where is defined by with , and one can get

Moreover, satisfies the following:

Then, since one can get where and in are any step functions. So the operator gives an isometry between the Hilbert space and . Hence, defined by is a space-time white noise, and has the following form:

Therefore, the mild formulation of (4) has the following form:

That is, the last term of (4) is equal to

The following embedding proposition is given by Nualart and Ouknine [27].

Lemma 1. Set , then, we have

3. Main Results and Their Proof

3.1. Main Results

For any function defined on , let where , , and . Let

Let be the functions which satisfy , endowed with the .

Define which is a Cameron-Martin space endowed with the norm

Suppose . Now, let be the solution of the following deterministic equation: where satisfies the following:

The above (26) shows a unique mild solution.

Similar to Jiang et al. [5], one can get the following:

Theorem 1. Let . Under Assumption1, the law of the solution to (1) satisfies a deviation principle on with the good rate function:with the convention , where .
More precisely, for any Borel measurable subset B of , where and denote the interior and the closure of , respectively.

We furthermore suppose that the coefficients satisfy the following.

Assumption 2. is differentiable, and the derivative of is Lipschitz. That is to say, there exist positive constant and which satisfy the following:

Together with the Lipschitz of , we conclude that

Now, we give the following central limit theorem.

Theorem 2. Let and its derivative satisfy Assumptions 1 and 2. Then, for , converges in to a random field on with , determined byfor all, .

Let the function be the solution to the following partial differential equation:

Under Assumptions 1 and 2, by Theorem 1, one can get which satisfies large deviation principles on with the speed and the good rate function satisfies the following:

Now, the second result is given as follows:

Theorem 3. In moderate deviation principle, let . Under the Assumptions1and2, then, the random field satisfies a large deviation principle on the space with speed and the good rate function defined by (36), where .

3.2. Convergence of the Solution

Lemma 2. Let . Under Assumption1, then, there exists a unique solution to (1). Moreover, for any ,

As , we get the convergence of as follows:

Proposition 1. Let and . By Assumption 1, there exists a constant which satisfies the following:

Proof. Note that One can get Set Together with Hölder’s inequality, the Lipschitz condition (C) and (5) of Lemma A.1, for , we have where .
For any , , , by (21) and (A.9), there exist which satisfies the following: Similarly, By (21), (A.10), and (A.11), for , one can get and Together with (43), (44), (45), and (46), one can get for any and , there exists a constant such that where is independent of . For , by Garsis-Rodemich-Rumser’s Lemma, there exist a constant C and a random variable satisfying and If we choose in (48), we can get By Gronwall’s inequality and (40), (42), and (48), there exists a constant satisfying The proof is completed.

3.3. The Proof of the Main Results

We first prove Theorem 1. Our proof is based on the following proposition (see Doss and Priouret [28]).

Proposition 2. Suppose and are two families of random variables and and are two Polish spaces. Suppose that(1)There exists a map such that is continuous for any .(2) satisfies with the rate functions .(3)For any , there exist and satisfying and for all , andThen, satisfies a LDP with the rate functions

To prove Theorem 1, one only needs to prove (i)Under some topology, is continuous for any .(ii)In Freidin-Wentzell inequality, for any , , and , there exist a satisfying

Theorem 4. When the level set is endowed with the topology of uniform convergence on ,is the continuous map for any .

Proof. One only needs to prove that for fixed , , Note that Using (A.5) in Appendix with , then , one can get Now, we deal with , together with Recalling is defined by (11), one can get Using Gronwall’s inequality, we can get The proof of the theorem is completed.

We now prove the Freidin-Wentizell inequality as follows:

Suppose is a Brownian sheet. For and , we define and

Using Girsanov’s theorem, the process is a Brownian sheet under . Suppose is a solution of (1) under . Then,

Now, one can prove (36). Note that, under , then,

So under , by Gronwall’s Lemma, one can get

Now, one can change (34) the proof to the following theorem.

Theorem 5. Suppose and . For each , , and , there exists a constant satisfying

In the following, we give a key Lemma to prove Theorem 1, which is similar to Candon-Weber [2], and the proof is omitted.

Lemma 3. Suppose , and satisfyingfor any and . Suppose which is an almost surely continuous, -adapted process satisfying , and for , supposeThen, for any , there exist a positive constant C, Ç(), and such that for allÇ(),

Proof of Theorem 5. Suppose Then, there exists satisfying If and , by Lemma 2, we can get The proof of Theorem 3 is completed.

In the following, we first give Garsis-Rodemich-Rumser’s Lemma in Bally et al. [29].

Lemma 4. Assume and are a family of real-valued stochastic processes. Suppose the following is true.

Assumption 3. One has as .

Assumption 4. For any , there exists satisfyingThen, for any ,

Now we can prove Theorem 2.

Proof of Theorem 2. Set . We will prove that To this end, we need to prove that Assumptions 3 and 4 are satisfied for . Note that where and Using Taylor’s formula, there exists a such that Note that is Lipschitz continuous and ; one can get So Using Hölder’s inequality, for and , one can get