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Dynamics of stochastic Boissonade system on the time-varying domain
Advances in Difference Equations volume 2016, Article number: 141 (2016)
Abstract
The current paper is devoted to studying the stochastic Boissonade system defined on time-varying domains. The existence and uniqueness of strong and weak solutions for the stochastic Boissonade system are established. Moreover, the existence of pullback attractor for the ‘partial-random’ system generated by the weak solution is also presented.
1 Introduction
The well-posedness and dynamics of the partial differential equations defined on the time-varying domains are interesting questions to study, and they have attracted a lot of attentions recently. There are many papers on this topic, we refer the reader to [1–10] and the references therein. The stochastic dynamical systems defined on time-varying domains are more attractive. Crauel, Kloeden, and Real established the framework for deterministic PDE on time-varying domains, and later, they also developed a new approach to defined noise on time-varying domain, and established the existence and uniqueness of the solutions for stochastic partial different equations with additive noise on time-varying domains in [11]. Recently, Crauel, Kloeden, and Yang developed the theory of ‘partial-random’ dynamical systems to obtain the existence of random attractors for stochastic reaction-diffusion equations on time-varying domains in [4].
Reaction-diffusion systems are usually used to describe the Turing pattern in a class of chemical or biological systems, and the Turing pattern was observed in the chlorite-iodine-malonic acid reaction in 1992. Dufiet and Boissonade in [12] were first to introduce the following reaction-diffusion systems (we called it a Boissonade system):
to exhibit the Turing pattern of the model to describe the relation between the genuine homogeneous 2D systems and the 3D monolayers, where \(d_{1}\), \(d_{2}\), α, γ, and β are positive constants.
The Boissonade system (1.1) is quite different from the Fitzhugh-Nagumo system in [13] and [14], the square term \(u^{2}\) in the Fitzhugh-Nagumo system is replaced by the cross term uv, leading to the nonlinearity of the second equation in the Boissonade system, and it induces more difficulties to obtain the uniqueness of the solution. Recently, Tu in [15] proved the existence of the global attractor for the Boissonade system (1.1). Due to the time-varying domain, the stochastic partial differential equation induces the new partial random dynamical systems, which is very interesting, we refer the reader to [11] for more details.
Motivated by the idea of Crauel, Kloeden, and Real in [3] and Crauel, Kloeden, and Yang in [11], we study the stochastic Boissonade system (SBS) on the time-varying domain by using some tricks derived from the Sobolev embedding theorem to obtain a unique solution for the SBS, and we establish the existence of a pullback attractor for the ‘partial-random’ dynamical system generated by the weak solution for the stochastic Boissonade system on the time-varying domain.
The rest of the paper is arranged as follows. In Section 2, some notations on time-varying domains are introduced. Sections 3 and 4 are devoted to proving the existence and uniqueness of solutions of random equations defined on fixed domains which are transformed from time-varying domains. The existence of the pullback attractor for the process generalized by the weak solution is presented in Section 5.
2 SBS defined on time-varying domains
In this section, we will introduce some notions and functional spaces on time-varying domains, following [11], and derive the Boissonade system with additive noise on the time-varying domain.
2.1 Assumption on the time-varying domain
Let \(\mathcal {O}\) be a nonempty bounded open subset of \(\mathbb {R}^{N}\) with \(C^{2}\) boundary \(\partial \mathcal {O}\), and \(r=r(y,t)\) a vector function
such that
\(\bar{r}(\cdot,t)=r^{-1}(\cdot,t)\) is the inverse of \(r(\cdot,t)\) satisfying the property
i.e., r̄, \(\frac{\partial\bar{r}}{\partial t}\), \(\frac {\partial\bar{r}}{\partial x_{i}}\) and \(\frac{\partial^{2}\bar{r}}{\partial x_{i}\,\partial x_{j}}\) belong to \(C(\bar{Q}_{\tau,T}; \mathbb {R}^{N})\) for all \(1\leq i\), \(j\leq N\) and for any \(\tau< T\). Then \(\{\mathcal {O}_{t}\}_{t\in[\tau,T]}\) is a family of nonempty bounded open subsets of \(\mathbb {R}^{N}\) (\(N\le3\)).
Define
and
For any \(T>\tau\), the set \(Q_{\tau,T}\) is an open subset of \(\mathbb {R}^{N+1}\) with the boundary
2.2 Assumption on noise
Assume that (Ω, \(\mathcal {F}\), \(\mathbb {P}\)) be a probability space, a sequence \(\{ w_{j}(t):t\in[0,\infty)\}_{j\ge1}\) of mutually independent two-sided standard scalar Wiener processes adapted to a common filtration \(\{\mathcal {F}_{t}:t\in[0,\infty)\}\) in \(\mathcal {F}\). Let \(\{\phi_{j}\}_{j\ge1}\subset H_{0}^{1}(\mathcal {O})\subset L^{2}(\mathcal {O})\) and \(\{ \varphi_{j}\}_{j\ge1}\subset H_{0}^{1}(\mathcal {O})\subset L^{2}(\mathcal {O})\) be two sequences of functions such that
Define
It follows from [7] that, for all \(t\in \mathbb {R}\),
Consider the \(L^{2}(\mathcal {O}_{t})\)-valued \(\mathcal {F}_{t}\)-adapted stochastic processes. Define
Let \(\mathbb {E}\) be the expectation with respect the probability \(\mathbb {P}\). Due to the pairwise independence of the \(w_{j}(t)\), we have
and
for any \(t\ge0\), \(m> n\ge1\). Therefore, we get \(M_{1}(t), M_{2}(t)\in L^{2}(\mathcal {O}_{t}\times\Omega)\) which are \(\mathcal {F}_{t}\)-measurable. Then \(\{ M_{1}(t):t\ge0\}\) and \(\{M_{2}(t):t\ge0\}\) can be viewed as \(\mathcal {F}_{t}\)-adapted processes with values in \(L^{2}(\mathcal {O}_{t})\).
Direct computation implies that \(\mathbb {E}M_{1}(t)=\mathbb {E}M_{2} (t)=0\),
and
for any \(t\in[0,\infty)\), where \(C_{r,t}=\max_{y\in\bar{\mathcal {O}}} \operatorname{Jac}(r,y,t)\) and \(\operatorname{Jac}(r,y,t)\) denoted the absolute value of the determinant of the Jacobi matrix \((\frac{\partial r_{i}}{\partial y_{j}}(y,t) )_{N\times N}\).
2.3 Stochastic Boissonade system on the time-varying domain
Following the arguments in [11], we can study the stochastic Boissonade system with additive noise and homogeneous Dirichlet boundary condition on the time-varying domain as follows:
where \(dM_{1}\) and \(dM_{2}\) can be represented by
and
Denote
and
Define \(b(y,t)=(b_{1}(y,t),\ldots,b_{N}(y,t))\in \mathbb {R}^{N}\) and \(c(y,t)=(c_{1}(y,t),\ldots,c_{N}(y,t))\in \mathbb {R}^{N}\) by
Then equations (2.7) on time-varying domains can be rewritten into the following equations on \(\mathcal {O}\times[0,\infty)\):
where
and
Due to the independence of the \(w_{j}\) and the assumption (2.5), the processes \(W_{1}(t)\) and \(W_{2}(t)\) are two \(H^{1}_{0}(\mathcal {O})\)-valued Wiener processes, and
and
Therefore, \(R_{1}(t)\) and \(R_{2}(t)\) are two \(\mathcal {F}_{t}\)-adapted processes belonging to \(L^{\infty}(0,T;L^{2}(\Omega\times \mathcal {O}))\) for all \(T\ge0\).
Denote
Then equations (2.11) can be transformed into the following equations (2.12):
In the following, in order to show the existence of strong solution, one is required to impose the conditions on \(\phi_{j}\) and \(\psi _{j}\), \(j=1,2,\ldots\) by
rather than the assumption in (2.5).
3 Existence of strong solutions of SBS (2.13)
In this section, we will establish the existence and uniqueness of the strong solution for equation (2.13).
For each \(T>0\), consider the auxiliary problem for equation (2.13),
Definition 3.1
(Strong solution)
A \(\mathcal {F}_{t}\)-adapted process \((F,G)=(F(\omega,y,t),G(\omega,y,t))\) defined in \(\Omega\times \mathcal {O}\times[0,T]\) is said to be a strong solution for problem (3.1) if
and the initial data conditions in (3.1) are satisfied almost everywhere in their corresponding domains.
Lemma 3.1
([6])
For any \(-\infty<\tau\le T<+\infty\), \(a_{jk}\in C^{1}(\bar{\mathcal {O}}\times [\tau,T])\), \(b_{k}\), \(c_{k}\in C^{0}(\bar{\mathcal {O}}\times[\tau,T])\). In particular, \(a_{jk}\), \(\frac{\partial a_{jk}}{\partial y_{j}}\), \(b_{k}\), \(c_{k}\in L^{\infty}(\mathcal {O}\times(\tau,T))\), \(j, k=1,2,\ldots,N\). Moreover, there exists a \(\delta=\delta(\nu,r,\tau,T)>0\) such that, for any \((y,t)\in \mathcal {O}\times[\tau,T]\),
Lemma 3.2
([6])
For any \(-\infty<\tau\le T<+\infty\), there exist two positive constants \(\delta_{0}\) and \(c_{0}\) which depend on r, Ï„, T such that for any \(u\in H^{2}(\mathcal {O})\cap H^{1}_{0}(\mathcal {O})\), the following estimate holds:
Define the time-dependent bilinear form by
for \(\alpha,\beta\in H_{0}^{1}(\mathcal {O})\) and \(0\le t\le T\).
We can apply the Galerkin argument(see[16–18]) to prove the existence of solution for SBS. Let \(\varpi_{k}=\varpi_{k}(y)\in H^{2}(\mathcal {O})\cap H_{0}^{1}(\mathcal {O})\) (\(k=1,2,\ldots \)) be the eigenfunctions of −△ on \(H_{0}^{1}(\mathcal {O})\), \(0<\lambda _{1}<\lambda_{2}<\cdots<\lambda_{n}\cdots\), \(\lambda_{n}\to\infty\) as \(n\to \infty\) be the corresponding eigenvalues. Then \(\{\varpi_{k}\}_{k=1}^{\infty}\) is an orthogonal basis of \(H_{0}^{1}(\mathcal {O})\) and an orthogonal basis of \(L^{2}(\mathcal {O})\).
For each fixed positive integer m, denote
Then for \(k=1,\ldots,m\) and \(\tau\leq t\leq T\),
where \(F_{0}(y):=u_{0}(r(y,t))-W_{1}\), \(G_{0}(y):=v_{0}(r(y,t))-W_{2}\). \((\cdot,\cdot)\) is the inner product in \(L^{2}(\mathcal {O})\) with associated norm \(\Vert \cdot \Vert _{L^{2}(\mathcal {O})}\), \(P_{m}\) is the projector from \(L^{2}(\mathcal {O})\) to span\(\{\varpi_{1},\varpi_{2},\ldots,\varpi_{m}\}\). It follows from [6] and the assumption (2.5) that \(F_{0}\in H_{0}^{1}(\mathcal {O})\), \(G_{0}\in H_{0}^{1}(\mathcal {O})\).
The assumption (2.14) yields
Noticing that for each integer \(m=1,2,\ldots\) , there exists a unique local \(\mathcal {F}_{t}\)-adapted process \((F_{m}(\omega), G_{m}(\omega))\) of (2.7) satisfying (\(A_{m}\)) in an interval \([0,T_{m}]\) with \(0\le T_{m}\le T\).
Next, we will show some estimates on the sequences \((F_{m}, G_{m})\), \(m=1,2,\ldots\) .
Lemma 3.3
The following estimates hold.
-
(1)
\(\{F_{m}\}\) is bounded in \(C^{0}([0,T];L^{2}(\Omega,L^{2}(\mathcal {O})))\cap L^{2}(0,T;L^{2}(\Omega,H_{0}^{1}(\mathcal {O})))\cap L^{4}([0,T]; L^{4}(\mathcal {O}\times \Omega))\),
-
(2)
\(\{G_{m}\}\) is bounded in \(C^{0}([0,T];L^{2}(\Omega,L^{2}(\mathcal {O})))\cap L^{2}(0,T;L^{2}(\Omega,H_{0}^{1}(\mathcal {O})))\).
Proof
Multiplying (\(A_{m}^{1}\)) by \(\zeta_{m}^{k}\) and (\(A_{m}^{2}\)) by \(\eta_{m}^{k}\), and taking the sum with respect to k from 1 to m, we obtain
Combing Lemma 3.1 with (3.4) and (3.5) guarantees that there exists a positive constant δ, which depends only on T such that \(\forall t\in[0,T_{m}]\), \(\mathbb {P}\)-a.s. \(\omega\in\Omega\),
where
and
Here, we just consider the following term:
Then it follows from Cauchy’s inequality that
where
and
By the fact \(\Vert P_{m} W_{1}\Vert _{H_{0}^{1}(\mathcal {O})}\le \Vert W_{1}\Vert _{H_{0}^{1}(\mathcal {O})}\), \(\Vert P_{m} W_{2}\Vert _{H_{0}^{1}(\mathcal {O})}\le \Vert W_{2}\Vert _{H_{0}^{1}(\mathcal {O})}\), the assumption (2.5) and the BDG inequality, we can find that \(\mathbb {E}R_{3}(t)<\infty\), \(\forall t\in[0,T_{m}]\). Then combining (3.10) with the Gronwall inequality and the fact \(\Vert P_{m}U_{0}\Vert _{L^{2}(\mathcal {O})}^{2}\le \Vert U_{0}\Vert _{L^{2}(\mathcal {O})}^{2}\), \(\Vert P_{m}V_{0}\Vert _{L^{2}(\mathcal {O})}^{2}\le \Vert V_{0}\Vert _{L^{2}(\mathcal {O})}^{2}\), we can find a positive constant \(M_{4}\) here such that
which implies that Lemma 3.3 holds. □
Lemma 3.4
The following estimates hold:
-
(3)
the sequence \(\{F_{m}\}\) is bounded in \(C^{0}([0,T];L^{2}(\Omega ,H_{0}^{1}(\mathcal {O})))\cap L^{2}(0,T;L^{2}(\Omega,H^{2}(\mathcal {O})))\),
-
(4)
the sequence \(\{G_{m}\}\) is bounded in \(C^{0}([0,T];L^{2}(\Omega ,H_{0}^{1}(\mathcal {O})))\cap L^{2}(0,T;L^{2}(\Omega,H^{2}(\mathcal {O})))\).
Proof
Multiplying (\(A_{m}^{2}\)) by \(\lambda_{k}\eta_{m}^{k}(t,\omega)\) and summing over \(k=1,2,\ldots\) , and recalling the fact that \(-\Delta_{y} G_{m}(t)=\sum_{k=1}^{m}\lambda_{k}\eta_{m}^{k}(t,\omega)\varpi_{k}\) equals 0 on \(\partial \mathcal {O}\), we obtain from Lemma 3.2
where \(M_{\bar{c}}=N^{1/2}\max_{1\le k\le N }\vert \bar {c_{k}}\vert _{L^{\infty}(\mathcal {O}\times(0,T))}\), and \(\bar{c}_{k}(y,t):=c_{k}(y,t)+d_{2}\sum_{j=1}^{N}\frac{\partial a_{jk}}{\partial y_{j}}(y,t)\), \(k=1,2,\ldots,N\).
By Cauchy’s inequality, one derives that
Since \(P_{m} G_{0}\) is bounded in \(H^{1}_{0}(\mathcal {O})\), then (2.14), (3.11), Lemma 3.3 and the Gronwall inequality imply that there exists a positive constant \(M_{5}\) that satisfies
Next, we show the second result in Lemma 3.4. Multiplying (\(A_{m}^{1}\)) by \(\lambda_{k}\zeta_{m}^{k}\), summing over \(k=1,2,\ldots,m\), we get
where \(M_{\bar{b}}=N^{1/2}\max_{1\le k\le N }\vert \bar {b_{k}}\vert _{L^{\infty}(\mathcal {O}\times(\tau,T))}\), and \(\bar{b}_{k}(y,t):=b_{k}(y,t)+d_{1}\sum_{j=1}^{N}\frac{\partial a_{jk}}{\partial y_{j}}(y,t)\), \(k=1,2,\ldots,N\). Here, we just consider the last term in (3.12),
The Cauchy inequality implies that
Then for \(N\le3\), the assumptions (2.5) and (2.14) imply that the second result of Lemma 3.4 holds. □
Lemma 3.5
The sequences \(\{F_{m}^{\prime}\}\), \(\{G_{m}^{\prime}\}\) are bounded in \(L^{2}(0,T;L^{2}(\Omega,L^{2}(\mathcal {O})))\).
Proof
Multiplying (\(A_{m}^{1}\)) by \(\zeta_{m}^{k\prime}\), summing over \(k=1,2,\ldots,m\), and combining with \(a_{k,j}=a_{j,k}\), we have
Similarly,
Noticing the fact that \(a_{k,j}\in C^{1}(\bar{\mathcal {O}}\times[\tau ,T])\) (\(k=1,2,\ldots,N\)), \(P_{m} F_{0}\), \(P_{m} G_{0}\) are bounded in \(H_{0}^{1}(\mathcal {O})\), \(N\le3\), we deduce that Lemma 3.5 holds. □
Theorem 3.1
Assume that r and r̄ satisfy the assumptions (2.1), (2.2), (2.3), and
Then for any \((u_{0},v_{0})\in H_{0}^{1}(\mathcal {O}_{0}) \times H_{0}^{1}(\mathcal {O}_{0}) \), \(\{ \phi_{j}(y)\}_{j=1,2,\ldots}\), \(\{\varphi_{j}(y)\}_{j=1,2,\ldots}\) satisfy the assumption (2.14), and for any \(0\le T<+\infty \), there exists a unique strong solution \((F,G)\) of (3.1). Moreover, \((F,G)\) satisfies the equality of energy, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\),
and
and the following estimates, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\):
where M is a constant and R is a fixed random function which satisfies, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\), \(R(t)\in L^{1}(0,T)\).
Proof
We first prove the uniqueness of the solution. Let \((u_{i0},v_{i0})\in H_{0}^{1}(\mathcal {O}_{0})\times H_{0}^{1}(\mathcal {O}_{0})\) and \((F_{i}(t),G_{i}(t))\), \(i=1,2\) be the corresponding strong solutions, then we derive
Taking the inner product of (3.18) with \((U_{1}-U_{2})\) and (3.19) with \(\alpha(V_{1}-V_{2})\) in \(L^{2}(\mathcal {O}_{t})\), we obtain, \(\mathbb {P}\)-a.s. \(\omega\in\Omega\),
where \(M_{b}\), \(M_{c}\) are defined by (3.9).
Thanks to the Hölder inequality
Since \((F_{1},G_{1})\) and \((F_{2},G_{2})\) are strong solutions of (3.1), \(U_{1}, V_{2}\in H_{0}^{1}(\mathcal {O})\), \(\forall t\in[\tau,T]\), and there exists a constant M such that \(\Vert U_{1}\Vert _{H_{0}^{1}(\mathcal {O})}\le M\) and \(\Vert V_{2}\Vert _{H_{0}^{1}(\mathcal {O})}\le M\). Applying the Sobolev embedding theorem, Cauchy’s inequality, and (3.21), we have
where M̃ is a constant dependent on \(d_{1}\), \(d_{2}\), M, α, γ, δ, and the Sobolev embedding constant.
Combining (3.22) with (3.20) yields
where \(M_{1}=\max\{1,\frac{1}{\alpha}\}*\max\{2\tilde{M}+(2+\frac {M_{b}^{2}}{d_{1}\delta}),2\tilde{M}+\alpha(\frac{M_{c}^{2}}{d_{2}\delta}-2\beta )\}\).
Due to the Gronwall lemma and the fact \(u_{10}(x)-u_{20}(x)=v_{10}(x)-v_{20}(x)=0\), \(F_{1}-F_{2}=U_{1}-U_{2}\), \(G_{1}-G_{2}=V_{1}-V_{2}\), we obtain the uniqueness of the strong solution for (3.1) immediately. Taking the inner product of (3.1) with \((U,V)\), we can obtain the energy equality (3.14) and (3.15) immediately.
Based on the estimates in Lemma 3.3, Lemma 3.4, and Lemma 3.5 on \(F_{m}\) and \(G_{m}\), there exist a subsequence of \(\{F_{m}(\omega)\}\) and a subsequence of \(\{ G_{m}(\omega)\}\) converging weakly in \(L^{2}((0,T]\times;H^{2}(\mathcal {O}))\), weakly star in \(L^{\infty}(0,T;H_{0}^{1}(\mathcal {O}))\), and strongly in \(L^{2}((0,T];H^{1}_{0}(\mathcal {O}))\), for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\). Moreover, the extremities \(F(\omega)\), \(G(\omega)\) are \(\mathcal {F}\)-adapted processes and satisfy
and
Thus, \(\{(F_{m},G_{m})\}\) converges to \((F,G)\) in the sense of mean-square.
Therefore, it follows that, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\),
where
Denote \(M=\max\{M_{2},M_{3}\}\); the Gronwall inequality implies Theorem 3.1 holds. □
4 Existence of the weak solution
In this section, we will show the existence of the weak solution for SBS.
Denote
Definition 4.1
For any given initial data \((u_{0},v_{0})\in(L^{2}(\mathcal {O}_{0}))^{2}\), \(0\le T<+\infty\), a function \((F,G)\) is called a weak solution of (3.1) if the following conditions hold. \(\mathbb {P}\)-a.s. \(\omega\in \Omega\),
-
(1)
\(F\in C([0,T];L^{2}(\mathcal {O}))\cap L^{2}([0,T];H_{0}^{1}(\mathcal {O}))\cap L^{4}(0,T;L^{4}(\mathcal {O}))\), \(G\in C([0,T];L^{2}(\mathcal {O}))\cap L^{2}([0,T];H_{0}^{1}(\mathcal {O}))\) with \((F(0),G(0))=(u_{0}(r(y,0))+W_{1}(0),v_{0}(r(y,0))+W_{2}(0))\).
-
(2)
There exists a sequence of regular data \((F_{0,m},G_{0,m})\in H_{0}^{1}(\mathcal {O})\times H_{0}^{1}(\mathcal {O})\), \(m=1,2,\ldots\) , such that \((F_{0,m},G_{0,m})\to(F_{0},G_{0})\) in \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\) and \((F_{m},G_{m})\to(F,G)\) in \(C([0,T];L^{2}(\mathcal {O}\times\Omega))\times C([0,T];L^{2}(\mathcal {O}\times\Omega))\).
-
(3)
It follows that, for all \(\vartheta\in\mathcal{U}_{0,T}\),
and
It is easy to find that every strong solution is a weak solution of (3.1) from the definition.
Theorem 4.1
Let the function r and r̄ satisfy assumptions (2.1)-(2.3). Assume that \(\partial \mathcal {O}\) is \(C^{m} m\ge2\). Then for any \((F_{0},G_{0})\in L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\) and \(0\le T<+\infty\), there exists a unique weak solution \((F,G)\) of (3.1). Moreover, \((F,G)\) satisfies the equality of energy, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\),
and the following estimates, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\):
where M and R are defined in the proof of Theorem 3.1.
Proof
We first of all show the uniqueness of weak solutions for (3.1). Let \((F_{1},G_{1})\) and \((F_{2},G_{2})\) be weak solutions for (3.1) with the initial value \((u_{0,1},v_{0,1})\) and \((u_{0,2},v_{0,2})\), respectively, then
Taking the inner product of (\(B_{2}\)) with \(V_{1}-V_{2}\) in \(L^{2}(\mathcal {O})\) and using Lemma 3.1 and Cauchy’s inequality, we obtain
where \(M_{c}\) is defined in the proof of Theorem 3.1.
Taking the inner product of (\(B_{1}\)) with \(U_{1}-U_{2}\) in \(L^{2}(\mathcal {O})\) and using Lemma 3.1 and Cauchy’s inequality again, we can get
Notice that \(U_{1},U_{2}\in C(0,T;L^{2}(\mathcal {O}))\), then there exists a constant \(M_{u}\) such that \(\vert U_{1}\vert ^{2}_{t}+\vert U_{2}\vert _{t}^{2}\le M_{u}\), \(\forall t\in(0,T)\), and
Hence, there exists a constant \(C_{N}\) such that
Combining the above inequality with (4.5) and (4.6), we obtain
where
Recalling that \(V_{1},V_{2}\in L^{2}(0,T;H_{0}^{1}(\mathcal {O}))\), so \(\int_{0}^{T} \bar{M}(s) \,dt< \infty\). Thus we can obtain uniqueness immediately from the above inequality, the Gronwall inequality, and the fact \(F_{1}-F_{2}=U_{1}-U_{2}\), \(G_{1}-G_{2}=V_{1}-V_{2}\), \(u_{10}=u_{20}\), \(v_{10}=v_{20}\).
Next, we will show the existence of a weak solution. Let \(F_{0,m},G_{0,m}\in H_{0}^{1}(\mathcal {O})\), \(m=1,2,\ldots\) , such that
Then for each \(F_{0,m}\), \(G_{0,m}\), \(m=1,2,\ldots\) , there exists a unique strong solution \((F_{m},G_{m})\) for (3.1). We deduce from (3.16) and (3.17) that, for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\),
and
which implies that
Therefore, we can extract a subsequence (denoted also by \(\{(F_{m},G_{m})\} \)) such that \(\mathbb {P}\)-a.s. \(\omega\in\Omega\)
Combining the arguments of the uniqueness and the fact (4.7), (4.8), which implies that \(\{F_{m}\}\) and \(\{G_{m}\}\) are Cauchy sequences in \(C([0,T];L^{2}(\mathcal {O}\times\Omega))\), the uniqueness of the limit and (4.12)-(4.13) yield, for \(\mathbb {P}\)-a.s. \(\omega\in \Omega\),
Therefore, extracting a subsequence if necessary, we can assume that \(\gamma F_{m}G_{m}-F_{m}^{3}\to\gamma FG-F^{3}\), a.e. in \(\mathcal {O}\times[0,T]\) as \(m\to\infty\). Then (4.14) implies that \(\Phi=\gamma FG-F^{3}\). Meanwhile, for any test function \(\vartheta\in\mathcal{U}_{0,T}\), \((F_{m},G_{m})\) satisfies (4.1) and (4.2). By using (4.12), (4.13), (4.14), and (4.15), and passing to the limit, we see that \((F,G)\) also satisfies (4.1) and (4.2). The estimates (4.3) and (4.4) can be obtained from (3.16), (3.17), (4.7), (4.8), and (4.15) directly. Thus, we can see \((F,G)\) is a weak solution of (3.1) with initial \((u_{0},v_{0})\) by all arguments above. Then the proof of Theorem 4.1 is completed. □
Remark 4.1
Since \((F,G)\in(L^{2}(0,T;H^{1}_{0}(\mathcal {O})))^{2}\), for any \(t\in(\tau,T)\), \(\mathbb {P}\)-a.s. \(\omega\in\Omega\), it follows that there exists an earlier time \(t_{0}\in(0,t)\) satisfying that \((F,G)\in(H^{1}_{0}(\mathcal {O}))^{2}\), which implies that the weak solutions of (3.1) turn into the strong solutions after a null measure set \((\tau,t_{0})\). Hence, we obtain \((F^{\prime},G^{\prime})\in L^{2}(0,T;L^{2}(\mathcal {O}))\times L^{2}(0,T;L^{2}(\mathcal {O}))\) and \((F,G)\in C(0,T;L^{2}(\mathcal {O}))\times C(0,T;L^{2}(\mathcal {O}))\).
Definition 4.2
A function \((F,G):\bigcup_{t\in[0,\infty)}\mathcal {O}\times{t} \to \mathbb {R}^{2}\) is called a weak solution of (2.13) if for any \(T\ge0\), the restriction of \((F,G)\) on \(\bigcup_{t\in [0,T)}\mathcal {O}\times{t}\) is a weak solution of (3.1).
Repeating arguments similar to Theorem 4.1, we obtain the following result.
Theorem 4.2
Under the same assumptions of Theorem 3.1, for any \((u_{0},v_{0})\in L^{2}(\mathcal {O}_{0})\times L^{2}(\mathcal {O}_{0})\), (2.13) has a unique weak solution.
5 The non-autonomous pullback \(\mathscr{D}_{\sigma}\)-attractor for SBS
In this section, we will establish some priori estimates for the solutions of (2.13), and introduce the ‘partial-random’ dynamical system generated by weak solution. By following the argument in [8], we prove the existence of the non-autonomous pullback \(\mathscr{D}_{\sigma}\)-attractor for the system.
Assume that \((F,G)\) is a weak solution of (2.13) with initial value \((F_{0},G_{0})\). Let
and
where b̄, c̄ are defined in the proof of Theorem 3.1. We will also assume that \(\bar{M}_{a}<\infty\), \(\bar{M}_{b}<\infty\), \(\bar{M}_{c}<\infty\), \(\bar{M}_{\bar{b}}<\infty\), and \(\bar{M}_{\bar{c}}<\infty\).
Lemma 5.1
There exist two positive constants M, C, and a random process \(R_{4}\) such that for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\), \(t\ge\tau\)
Proof
Taking the inner product of the first formula of (2.13) with F and the second formula with G in \(L^{2}(\mathcal {O})\), then using Cauchy’s inequality, Hölder’s inequality, and Lemma 3.1, we can obtain
where \(M_{1}=\frac{2\bar{M}_{b}^{2}}{d_{1}\delta}+\bar{M}_{b}+2\alpha+\gamma +4\), \(M_{2}=\alpha+5\gamma^{2}\), and
Similarly, we have
where \(M_{3}=\frac{2\bar{M}_{c}^{2}}{d_{2}\delta}+\bar{M}_{c}+4-\beta\), and
Choosing \(\beta>\max_{\delta}\{\frac{2\bar{M}_{c}^{2}}{d_{2}\delta}+\bar{M}_{c}+4\}\) such that \(M_{3}<0\), and denoting \(\tilde{M}_{3}=-M_{3}\). We can derive from (5.5) and (5.6) that
Let \(\bar{C}=\frac{2M_{2}}{\tilde{M}_{3}}\), then
Denote
the inequality (5.8) implies that
From the Gronwall inequality, we see that for \(\mathbb {P}\)-a.s. \(\omega\in \Omega\)
Denoting \(M=\frac{\max\{1,\bar{C}\}}{\min\{1,\bar{C}\}}\), \(C=\frac {M_{2}}{\bar{C}}\) and \(R_{4}(t,\omega)=\frac{1}{\min\{1,\bar{C}\}} R_{7}(t,\omega)\), the proof is completed. □
Lemma 5.2
For any nonrandom bounded set \(B\in L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\), there exists a random time \(T_{B}(\omega)\geq0\) such that
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\), for all \(t-\tau\ge T_{b}(\omega )\), for any \((F(\tau),G(\tau))\in B\).
Proof
It follows from Lemma 5.1 that
We can obtain from the above inequality that
Then there exists a random time \(T_{B}(\omega)\) such that for \(t-\tau \ge T_{B}(\omega)\)
Thus, the proof is completed. □
Corollary 5.1
For any nonrandom bounded \(B\in L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\), there exist a random time \(T_{B}(\omega)\geq0\) such that
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\), for all \(t-\tau\ge T_{B}(\omega )\), for any \((F(\tau),G(\tau))\in B\).
Lemma 5.3
For any nonrandom bounded \(B\in L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\), there exists a random time \(\bar{T}_{B}(\omega)\geq0\) and a random constant M̃, such that
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\), for all \(t-\tau\ge T_{B}(\omega )\), for any \((F(\tau),G(\tau))\in B\).
Proof
Taking the inner product of the second formula in (2.13) with \(-\triangle G\) in \(L^{2}(\mathcal {O})\), and using Cauchy’s inequality, Hölder’s inequality, and Lemma 3.2, we obtain
Combining the assumptions (2.5), (2.14) with Lemmas 5.1-5.2, we have
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\) and for all \(t-\tau>T_{B}(\omega )\). Therefore there exists a constant M̃ such that
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\) and for all \(t-\tau>T_{B}(\omega)+1\).
Similarly,
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\) and for all \(t-\tau>T_{B}(\omega )+1\). Thus, applying the uniform Gronwall lemma to (5.16), we see that there exists a constant M̃ such that
for \(\mathbb {P}\)-a.s. \(\omega\in\Omega\) and for all \(t-\tau>T_{B}(\omega )+2\). Denoting \(\bar{T}_{B}(\omega)=T_{B}(\omega)+2\), we complete the proof. □
6 Attractors for partial-random dynamical system
In this section, we introduce the partial-random dynamical system generated by a SPDE defined on time-varying domains developed by Crauel et al. in [4], and prove the existence of the non-autonomous attractor for partial-random dynamical system.
Assume that the probability space \((\Omega, \mathcal {F},\mathbb {P})\) with incremental shifts \((\kappa_{t})_{t\in \mathbb {R}}\) is a metric dynamical system, \(\mathfrak {R}\) is a subset of the topology of space \(C_{b}^{1}(\mathbb {R};C_{b}^{2}(\bar{\mathcal {O}};\mathbb {R}^{N}))\) generated by the domain varying diffeomorphisms r. The transformations \(\pi_{t}:\mathfrak {R}\to \mathfrak {R}\) defined by \(\pi_{t} r(\cdot +s,\cdot)=r(\cdot+s+t,\cdot)\) for \(t\in \mathbb {R}\), form a one-parameter group \((\pi_{t})_{t\in \mathbb {R}}\) with
for all \(s,t\in \mathbb {R}\). The product flow, given by
for \(t\in \mathbb {R}\), will be denoted by \((\bar{\kappa}_{t})_{t\in \mathbb {R}}\).
For each \((F_{0},G_{0})\in(L^{2}(\mathcal {O}))^{2}\), Theorem 4.2 implies that equations (2.13) have a unique global solution \((F,G)\). Define the operators
by
Here \((F(t;(\omega,r),F_{0},G_{0}),G(t;(\omega,r),F_{0},G_{0}))\) is defined by unique solution process of (5.9) with initial value \((F_{0},G_{0})\) and the transform for domains r. From Theorem 4.2, we know that the definition makes sense. Then the family of operators \(\{ \Upsilon(t):0\le t<+\infty\}\) generates a non-autonomous dynamic system, i.e.
Now, we can define the attractor of the non-autonomous dynamic system Ï’.
Definition 6.1
([4])
Suppose that \(\mathcal {D}\) is a set of maps from \(\Omega\times \mathfrak {R}\) to the power set of \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\) such that \(D(\omega ,r)\) is nonempty for every \((\omega,r)\in\Omega\times \mathfrak {R}\) and \(D\in\mathcal {D}\). A map A from \(\Omega\times \mathfrak {R}\) to the power set of \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\) is said to be a \(\mathcal {D}\)-attractor if:
-
(1)
\(A(\omega,r)\) is compact for all \((\omega,r)\in\Omega \times \mathfrak {R}\),
-
(2)
A is invariant in the sense that
$$\Upsilon\bigl(t,(\omega,r)\bigr)A(\omega,r)=\bar{\kappa}_{t}A(\omega,r) $$for all \(t \in[0,\infty)\) and \((\omega,r)\in\Omega\times \mathfrak {R}\),
-
(3)
A attracts every \(D\in\mathcal {D}\) in the sense that
$$\lim_{t\to\infty}\operatorname{dist}\bigl(\Upsilon\bigl(t,\bar{\kappa}_{-t}( \omega,r)\bigr)D\bigl(\bar{\kappa}_{-t}(\omega,r)\bigr),A(\omega,r) \bigr)=0 $$for every \(D\in\mathcal {D}\).
Here \(\operatorname{dist}(A,D)\) is for the Hausdorff semi-distance.
Definition 6.2
([4])
Suppose that \(\mathcal {D}\) is a set of maps from \(\Omega\times \mathfrak {R}\) to the power set of \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\) such that \(D(\omega ,r)\) is nonempty for every \((\omega,r)\in\Omega\times \mathfrak {R}\) and \(D\in\mathcal {D}\). A map K from \(\Omega\times \mathfrak {R}\) to the power set of \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\) is said to be a \(\mathcal {D}\)-attracting if
for every \(D\in\mathcal {D}\).
Theorem 6.1
([4])
The existence of a compact \(\mathcal {D}\)-attracting K is equivalent to the existence of a \(\mathcal {D}\)-attractor.
Remark 6.1
From Lemmas 5.2 and 5.3, we can find that there exists a compact \(\mathcal {D}\)-attracting K for the non-autonomous dynamic system Ï’ defined above, attracting bounded subsets of \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\). Thus, using Theorem 6.1, we can obtain a unique non-autonomous pullback attractor in \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\).
Theorem 6.2
The partial-random system generated by the random-PDE (2.13) on domain \(\mathcal {O}\) has a unique non-autonomous pullback attractor in \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\), attracting bounded subsets of \(L^{2}(\mathcal {O})\times L^{2}(\mathcal {O})\).
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Acknowledgements
The authors would like to thank the anonymous referees and editors for their helpful suggestions, which have improved the quality of the paper largely. This work was supported by the NSF of China (No.11371367, 11571126).
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Zhang, Z., Huang, J. Dynamics of stochastic Boissonade system on the time-varying domain. Adv Differ Equ 2016, 141 (2016). https://doi.org/10.1186/s13662-016-0861-z
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DOI: https://doi.org/10.1186/s13662-016-0861-z
MSC
- 35K57
- 35K90
- 37L30
Keywords
- pullback attractor
- partial-random dynamical systems
- stochastic Boissonade system
- time-varying domain