The weak convergence method of proving a large deviation principle has been developed by Dupuis and Ellis in  . The main idea is to get sevral variational representation formulas for the Laplace transform of certain functionals, and then to prove an equi- valence between Laplace principle and large deviation principle (LDP). For Brownian functionals, Boué and Dupuis  have proved an elegant variational representation formula (also can be found in Zhang  ). For Poisson functionals, we can see Zhang  . Recently, a variational representation formula on Wiener-Poisson space has been estab- lished by Budhiraja, Dupuis, and Maroulas in  . These type variational representations have been proved to be very effective for both finite-dimensional and infinite-dimen- sional stochastic dynamical systems (cf.  -  ). The main advantages of this method are that we only have to make some necessary moment estimates.
However, there are still few results on the large deviation for stochastic evolution equations with jumps. In  , Röckner and Zhang considered the following type semi-linear stochastic evolutions driven by Lévy processes
they established the LDP by proving some exponential integrability on different spaces. Later, Budhiraja, Chen and Dupuis developed a large deviation for small Poisson perturbations of a more general class of deterministic equations in infinite dimensional (  ), but they did not consider the small Brownian perturbations simultaneously.
Motivated by the above work, we would like to prove a Freidlin-Wentzell’s large deviation for nonlinear stochastic evolution equations with Poisson jumps and Brownian motions. At the same time, nonlinear stochastic evolution equations have been studied in various literatures (cf.  -  ). So we consider the following stochastic evolution equation:
in the framework of a Gelfand’s triple:
where V, H (see Section 2) are separable Banach and separable Hilbert space respec- tively. We will establish LDP for solutions of above evolution equation on
, where is H-valued cádlág function space with the Skorokhod topology. For stochastic evolution equations without jumps, Ren and Zhang  and Liu  achieved the LDP on () and () respectively. In our case, there are two new difficulties. The first one is to find a sufficient condition to characterize a compact set in (see Proposition 4) instead of Ascoli-Arzelà’s theorem for continuous case, the second one is to control the jump parts. This form of equation contains a large class of (nonliear) stochastic partial differential equation of evolutional type, for applications and examples we refer the reader to  ,  . The equations we consider here are more general than the equations considered in  , and we use a different method. We note that, the large deviations for semilinear SPDEs in the sense of mild solutions were considered in paper  recently. For other recent research on this topic, see also  ,  .
In Section 2, we firstly give some notations and recall some results from  , which are the basis of our paper, and then introduce our framework. In Section 3, we prove the large deviation principle. In the last section, we give an application. Note that notations c, and below will only denote positive constants whose values may vary from line to line.
2. Preliminaries and Framework
We first recall some notations from  .
Let be a locally compact Polish space and denote by the space of all measures on, satisfying for every compact. Let
be the space of continuous functions with compact support. is a Polish space endowed with the weakest topology such that for every,
is a continuous function.
Set. Fix and let. Let and denote by the unique probability measure on such that the canonical map, , , is a Poisson random measure with intensity
, where, and are Lebesgue measures on and respectively.
Let G be a real separable Hilbert space and let Q be a positive definite and symmetric trace operator defined on G. Set and. Let be defined by, for. Let W be the coordinate map on defined as. Define. We denote by P the unique probability measure on such that under P:
1) W is a Q-Wiener process;
2) N is a Poisson random measure with intensity measure;
3), are -martingales for every.
We denote by be P-completion of the filtration. From now on, we will work on the probability space with filtration.
Denote by the predictable s-field on with the filtration
., de- fine
and define a counting process as
For fixed, let
By  , we can define, for a function, and identify g with measure. Besides, is a compact subset of through the superlinear groth of l. We can also consider the to- pology on which makes a compact space.
Remark 1. We note that, for, , in this topology means
, that is, for any,
Set and define. Let
We endow with the weak topology on the Hilbert space such that is a compact subset of.
Let with the usual product topology. Set and let be the space of -valued controls:
Let be a Polish space and let be a set of -valued random variables
defined on by
where is a family of measurable maps from to.
Hypothesis. There exists a measurable map such that the following hold.
1) For, if a family converges in distribution to, then
where Þ denotes the weak convergence.
2) For, let be such that. Then
For, define. Let be
We have the following important result due to  .
Theorem 2. Under the above Hypothesis, satisfies a large deviation prin- ciple with rate function I.
Now we introduce our framework and assumptions.
Let be a real separable Hilbert space. Let V be a reflexive Banach space and be the dual space of V and denotes the corresponding dualization. Identify H with its dual and the following assumptions are satisfied:
2) V is dense in H;
3) there exists a constant c such that for all,;
Let be the space of Hilbert-Schmidt linear operators from G to H, which is a real separable Hilbert space with the inner product
where is an orthonormal basis of G. We denote by the set of all linear operators C mapping into H such that, and the norm.
be progressively measurable. For example, for every, A restricted to
We assume throughout this paper that:
(H1) Hermicontinuity: For any, and any, the mapping
(H2) Weak monotonicity: There exist such that for all
(H3) Coercivity: For all and, there exist such that
(H4) For all and, there exists such that
(H5) There exists such that for all, and
(H6) There exist some compact, , for all
. For any, is continuous on.
3. Large Deviation Principle
Consider small noise stochastic evolution equation as following:
Under the assumptions (H1)-(H5), by  ,  , there exists a unique solution in to Equation (5). By Yamada-Watanabe theorem, there exists a measurable mapping such that
We now fix a family of processes, and put
By Girsanov’s theorem, is the unique solution of the following controlled sto- chastic evolution equation:
Remark 3. For, by (1) and (2), there exists a constant such that for all,
We will verify that satisfies the Hypothesis with replaced by
. By using the similar method as in  , we have the following uniform estimates about.
Lemma 1. There exists a constant such that, for all,
In order to characterize a compact set in, we need the following lemma.
Lemma 2. For any and, there exist and such that for any, we have
Proof. For fixed and any t such that, we have
For, by (H4), Hölder’s inequality and Lemma 1, we have
By (7), we have
So by (9) and dominated convergence theorem, for all, we obtain
For, , by BDG’s inequality, (H5) and Lemma 1, we obtain
Hence, for any
By choosing and small enough, then (10) holds immediately.
Proposition 4. For a sequence of -valued random variable, if satisfies the following two conditons:
1) For any, there are, , with
2) For any and, there are, , with
Then is C-tight, that is, is tightness in and if X is a limit point then a.s..
Proof. It’s obvious that (2) implies the following condition (cf.  , p. 290). For any and, there are, , with
For the finite family, we can find and such that
Hence, replacing R by in (1) and by in (11), we obtain that they still hold with.
Fix. Let and satisfy
It remains to prove that if a subsequence, still denoted by, converges in law to some X, then X is a.s. continuous. By taking the same scheme as in Proposition 3.26 (cf.  , p. 315) and replacing by in the proof, we complete the proof.
According to Lemma 1 and Lemma 2, we have the following result:
Corollary 1. The sequence is C-tight in.
Lemma 3. Assume that for almost all, weakly converges to in for fixed and there is a -valued process such that
Then, solves the following equation:
Moreover, we have
and if in (H2), then
Proof. We divide our proof into several steps.
Step 1. By Lemma 1, we have
Therefore, by the strong convergence of to as in (12). We get, for almost all, converges weakly to in H and converges to weakly in; and so we have
By (12), (16) and dominated convergence theorem, we have
Step 2. In this step, we prove solves Equation (13). By (H4) and (15), we have
Hence, by (15) and (20), there exist subsequences of, and (still denoted by themselves for simplicity) and, and such that
By taking weak limits and by (19), we can get
Indeed, for any V-valued bounded and measurable process,
By (21), (23) and taking limits for, then we get (see also the proof of (27) and (29) below)
which implies for almost all. Similarly, we have
for almost all.
We only have to prove
Let. By Itô’s formula
We now prove
Since weakly converges to in (see (2)), then
the last limit follows by using dominated convergence theorem. By (2), (H5), Lemma 1 and (19), we also have
Then limit (27) follows.
Moreover, it is easy to get that
Now we prove the following limit:
By (H5), Lemma 1 and (19), we have
For, by Young inequality, we have
by noting (16) and (19). For, by (4), (H6) and, it’s easy to verify
is a continuous function on with the compact su-
pport, and by the weak convergence of to (see Remark 1) and domi- nated convergence theorem, as. Then (30) goes to 0 as. Similarly, we have
Then, we get (29).
It is obvious that
Combining (26) to (31) yields that
On the other hand, by Itô’s formula we have
So, we have
which implies (24) by (H1).
Step 3. In this step we prove (13) and (14). Notice that
By Itô’s formula, we have
By Lemma 1 and BDG’s inequality, we get
For, we have
For, like, we have
For, by (H5) and (H6) we have
Notice (32), we get (13) and (14) immediately.
We also have the following main lemma.
Lemma 4. There exists a probability space and a sequence (for conve-
nience, still denote by) and
defined on this space and taking value in
with such that:
1) For each, has the same law as
2) in, -a.s., as;
3) uniquely solves the following equation:
Moreover, we have
and if, then
Proof. From Corollary 1, we have is C-tight in. By the com- pactness of, the law of in is tight. By Skorok- hod’s embedding theorem, (1) and (2) hold. Since -a.s. and
Then, the other conclusions follow from Lemma 3 and noting for almost all,.
Remark 5. Assume that (H1)-(H7) and hold, we have verified Hypothesis (1) by the above lemma.
For fixed, let and let such that is the unique solution of
We point out that the difference between in the above equation and in (13) is that is not random. We have the following result.
Lemma 5. Assume that (H1)-(H7) and hold. Let, be such that in the weak topology of (see Section 2), then
Proof. Similar to the proofs of Lemma 1 and 2, we can get
is C-tight. As in Lemma 4, there exist a subsequence (still denoted by m) and
Combining with this convergence and the method used in the proof of Lemma 3, we have, then the result holds.
Using Remark 5, Lemma 5 and Theorem 2, we obtain the following large deviation principle.
Theorem 6. Under the same assumptions as in Lemma 5, satisfies a large deviation principle with rate function I defined as in (3), i.e. for any
where is the law of in and is.
Remark 7. If, then the conclusion still holds if is replaced by
4. Application―Stochastic Porous Medium Equation
Similar to  , consider a bounded domain in with smooth boundary. For
The inner product in H is defined by
establish an isomorphism between and. We identify
with the dual space and H, then. There- fore
and the inclusions are compact.
Let. For, denote by
Then and (H1)-(H4) hold (cf.   ).
where are Lipschitz continuous on. Let, , and define
where are Lipschitz continuous on. Then B and f satisfy (H5)-(H6).
Consider the following stochastic porous medium equation
Let be the law of in. Then the conclusion of Theorem 6 holds.
The authors thank the Editor and the referee for their valuable comments. This work is supported in part by Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ13A010020) and the National Natural Science Foundation of China (Grant No. 11401029).
 Dupuis, P. and Ellis, R.S. (1997) A Weak Convergence Approach to the Theory of Large Deviations. Wiley Series in Probability and Statistics: Probability and Statistics. A Wiley-Interscience Publication. John Wiley and Sons, Inc., New York.
 Boué, M., Dupuis, P. and Ellis, R.S. (2000) Large Deviations for Small Noise Diffusions with Discontinuous Statistics. Probability Theory and Related Fields, 116, 125-149.
 Zhang, X. (2008) Euler Schemes and Large Deviations for Stochastic Volterra Equations with Singular Kernels. Journal of Differential Equations, 224, 2226-2250.
 Röckner, M. and Zhang, T. (2007) Stochastic Evolution Equations of Jump Type: Existence, Uniqueness and Large Deviation Principles. Potential Analysis, 26, 255-279.
 Budhiraja, A., Chen, J. and Dupuis, P. (2013) Large Deviations for Stochastic Partial Differential Equations Driven by a Poisson Random Measure. Stochastic Processes and their Applications, 123, 523-560.
 Ren, J., Röckner, M. and Wang, F. (2007) Stochastic Generalixed Porous Media and fast Diffusion Equation. Journal of Differential Equations, 238, 118-152.