In this paper, we propose a novel staggered least squares method for elliptic equations on polygonal meshes. Our new method can be flexibly applied to rough grids and allows hanging nodes, which is of particular interest in practical applications. Moreover, it offers the advantage of not having to deal with inf-sup conditions and yielding positive definite discrete problems. Optimal a priori error estimates in energy norm are derived. In addition, a superconvergent estimates in energy norm are also developed by employing variational error expansion. The main difficulty involved here is to show the L2 norm error estimates for the potential variable, where duality argument and the superconvergent estimates are the key ingredients. The single valued flux over the outer boundary of the dual partition enables us to construct a locally conservative flux. Numerical experiments confirm the theoretical findings and the performance of the adaptive mesh refinement guided by the least squares functional estimator are also displayed.
Citation: Lina Zhao, Eun-Jae Park. A locally conservative staggered least squares method on polygonal meshes[J]. Mathematics in Engineering, 2024, 6(2): 339-362. doi: 10.3934/mine.2024014
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In this paper, we propose a novel staggered least squares method for elliptic equations on polygonal meshes. Our new method can be flexibly applied to rough grids and allows hanging nodes, which is of particular interest in practical applications. Moreover, it offers the advantage of not having to deal with inf-sup conditions and yielding positive definite discrete problems. Optimal a priori error estimates in energy norm are derived. In addition, a superconvergent estimates in energy norm are also developed by employing variational error expansion. The main difficulty involved here is to show the L2 norm error estimates for the potential variable, where duality argument and the superconvergent estimates are the key ingredients. The single valued flux over the outer boundary of the dual partition enables us to construct a locally conservative flux. Numerical experiments confirm the theoretical findings and the performance of the adaptive mesh refinement guided by the least squares functional estimator are also displayed.
This paper deals with periodic measures of the following reaction-diffusion lattice systems driven by superlinear noise defined on the integer set Zk :
dui(t)+λ(t)ui(t)dt−ν(t)(u(i1−1,i2,…,ik)(t)+ui1,i2−1,…,ik(t)+…+ui1,i2,…,ik−1(t)−2ku(i1,i2,…,ik)(t)+u(i1+1,i2,…,ik)(t)+u(i1,i2+1,…,ik)(t)+…+u(i1,i2,…,ik+1)(t))dt=fi(t,ui(t))dt+gi(t)dt+∞∑j=1(hi,j(t)+δi,jˆσi,j(t,ui(t)))dWj(t), | (1.1) |
along with initial conditions:
ui(0)=u0,i, | (1.2) |
where i=(i1,i2,…,ik)∈Zk, λ(t),ν(t) are continuous functions, λ(t)>0, (fi)i∈Zk and (ˆσi,j)i∈Zk,j∈N are two sequences of continuously differentiable nonlinearities with arbitrary and superlinear growth rate from R×R→R, respectively, g=(gi)i∈Zk and h=(hi,j)i∈Zk,j∈N are two time-dependent random sequences, and δ=(δi,j)i∈Zk,j∈N is a sequence of real numbers. The sequence of independent two-sided real-valued Wiener processes (Wj)j∈N is defined on a complete filtered probability space (Ω,F,{Ft}t∈R,P). Furthermore, we assume that system (1.1) is a time periodic system; more precisely, there exists T>0 such that the time-dependent functions λ,ν,fi,g,h,σi,j(i∈Zk,j∈N) in (1.1) are all T-periodic in time.
Lattice systems are gradually becoming a large and evolving interdisciplinary research field, due to wide range of applications in physics, biology and engineering such as pattern recognition, propagation of nerve pulses, electric circuits, and so on, see [1,2,3,4,5,6] and the references therein for more details. The well-posedness and the dynamics of these equations have been studied by many authors, [7,8,9,10] for deterministic systems and [11,12,13,14,15,16,17,18,19] for stochastic systems where the existence of random attractors and probability measures have been examined. Especially, the authors research the limiting behavior of periodic measures of lattice systems in [15].
Nonlinear noise was proposed and studied for the first time in [19], the authors researches the long-term behavior of lattice systems driven by nonlinear noise in terms of random attractors and invariant measures. Before that, the research on noise was limited to additive noise and linear multiplicative noise, which can be transformed into a deterministic system. However, if the diffusion coefficients are nonlinear, then one cannot convert the stochastic system into a pathwise deterministic one, and thereby this problem cannot be studied under the frameworks of deterministic systems aforementioned. As an extension of [19], a class of reaction-diffusion lattice systems driven by superlinear noise, where the noise has a superlinear growth order q∈[2,p), is studied by taking advantage of the dissipativeness of the nonlinear drift function fi in (1.1) to control the superlinear noise in [20].
In the paper, we will study the existence of periodic measures of reaction-diffusion lattice systems drive by superlinear noise. One of the main tasks in our analysis is to solve the superlinear noise terms. We remark that if the noise grows linearly, then the estimates we need can be obtained by applying the standard methods available in the literature. We adopt the ideas that take advantage of the nonlinear drift terms' the polynomical growth rate p (p≥2) to control the noise polynomical rate q∈[2,p). Furthermore, notice that l2 is an infinite-dimensional phase space and problem (1.1)–(1.2) is defined on the unbounded set Zk. The unboundedness of Zk as well as the infinite-dimensionalness of l2 introduce a major difficulty, because of the non-compactness of usual Sobolev embeddings on unbounded domains. We will employ the dissipativeness of the drift function in (1.1) as well as a cutoff technique to prove that the tails of solutions are uniformly small in L2(Ω,l2). Based upon this fact we obtain the tightness of distribution laws of solutions, and then the existence of periodic measures.
In the next section, we discuss the well-poseness of solutions of (1.1) and (1.2). Section 3 is devoted to the uniform estimates of solutions including the uniform estimates on the tails of solutions. In Section 4, we show the existence of periodic measures of (1.1) and (1.2).
In this section, we prove the existence and uniqueness of solutions to system (1.1) and (1.2). We first discuss the assumptions on the nonlinear drift and diffusion terms in (1.1).
We begin with the following Banach space:
lr={u=(ui)i∈Zk:∑i∈Zk|ui|r<+∞} with norm ‖u‖r=(∑i∈Zk|ui|r)1r,∀r≥1. |
The norm and inner product of l2 are denoted by (⋅,⋅) and ‖⋅‖, respectively. For the nonlinear drift function fi∈C1(R×R,R) in the equation we assume that for all s∈R and i∈Zk,
fi(t,s)s≤−γ1|s|p+ϕ1,i, ϕ1={ϕ1,i}i∈Zk∈l1, | (2.1) |
|fi(t,s)|≤ϕ2,i|s|p−1+ϕ3,i, ϕ2={ϕ2,i}i∈Zk∈l∞, ϕ3={ϕ3,i}i∈Zk∈l2, | (2.2) |
|f′i(t,s)|≤ϕ4,i|s|p−2+ϕ5,i, ϕ4={ϕ4,i}i∈Zk∈l∞, ϕ5={ϕ5,i}i∈Zk∈l∞, | (2.3) |
where p>2 and γ1>0 are constants. For the sequence of continuously differentiable diffusion functions ˆσ=(ˆσi,j)i∈Zk,j∈N, we assume, for all s∈R and j∈N,
|ˆσi,j(t,s)|≤φ1,i|s|q2+φ2,i, φ1={φ1,i}i∈Zk∈l2pp−q, φ2={φ2,i}i∈Zk∈l2, | (2.4) |
|ˆσ′i,j(t,s)|≤φ3,i|s|q2−1+φ4,i, φ3={φ3,i}i∈Zk∈lq, φ4={φ4,i}i∈Zk∈l∞, | (2.5) |
where q∈[2,p) is a constant. For processes g(t)=(gi(t))i∈Zk and h(t)=(hi,j)i∈Zk,j∈N are both continuous in t∈R, which implies that for all t∈R,
‖g(t)‖2=∑i∈Zk|gi(t)|2<∞ and ‖h(t)‖2=∑i∈Zk∑j∈N|hi,j(t)|2<∞. | (2.6) |
In addition, we assume δ=(δi,j)i∈Zk,j∈N satisfies
cδ:=∑j∈N∑i∈Zk|δi,j|2<∞. | (2.7) |
We will investigate the periodic measures of system (1.1)–(1.2) for which we assume that all given time-dependent functions are T-periodic in t∈R for some T>0; that is, for all t∈R,i∈Zk and k∈N.
λ(t+T)=λ(t),ν(t+T)=ν(t),h(t+T)=h(t),g(t+T)=g(t),f(t+T,⋅)=f(t,⋅),σ(t+T,⋅)=σ(t,⋅). |
If m:R→R is a continuous T-periodic function, we denote
¯m=max0≤t≤Tm(t),m_=min0≤t≤Tm(t). |
We want to reformulate problem (1.1)–(1.2) as an abstract one in l2. Given 1≤j≤k,u=(ui)i∈Zk∈l2 and i=(i1,i2,…,ik)∈Zk. Let us define the operators from l2 to l2 by
(Bju)i=u(i1,…,ij+1,…,ik)−u(i1,…,ij,…,ik),(B∗ju)i=u(i1,…,ij−1,…,ik)−u(i1,…,ij,…,ik),(Aju)i=−u(i1,…,ij+1,…,ik)+2u(i1,…,ij,…,ik)−u(i1,…,ij−1,…,ik), |
and
(Aku)i=−u(i1−1,i2,…,ik)−u(i1,i2−1,…,ik)−…−u(i1,i2,…,ik−1)+2ku(i1,i2,…,ik)−u(i1+1,i2,…,ik)−u(i1,i2+1,…,ik)−…−u(i1,i2,…,ik+1). |
For all 1≤j≤k,u=(ui)i∈Zk∈l2 and v=(vi)i∈Zk∈l2 we see
‖Bju‖≤2‖u‖,(B∗ju,v)=(u,Bjv),Aj=BjB∗j and Ak=k∑j=1Aj. | (2.8) |
Again, define the operators f,σj:R×l2→l2 by
f(t,u)=(fi(t,ui))i∈Zk and σj(t,u)=(δi,jˆσi,j(t,ui))i∈Zk,∀t∈R,∀u=(ui)i∈Zk∈l2. |
It follows from (2.3) that there exists θ∈(0,1) such that for p>2 and u,v∈l2,
∑i∈Zk|fi(t,ui)−fi(t,vi)|2=∑i∈Zk|f′i(θui+(1−θ)vi)|2|ui−vi|2≤∑i∈Zk(|ϕ4,i||θui+(1−θ)vi|p−2+|ϕ5,i|)2|ui−vi|2≤∑i∈Zk(22p−4|ϕ4,i|2(|ui|2p−4+|vi|2p−4)+2|ϕ5,i|2)|ui−vi|2≤(22p−4‖ϕ4‖2l∞(‖u‖2p−4+‖v‖2p−4)+2‖ϕ5‖2l∞)‖u−v‖2. | (2.9) |
This together with f(t,0)∈l2 by (2.2) yields f(t,u)∈l2 for all u∈l2, and thereby f:R×l2→l2 is well-defined. In addition, we deduce from (2.9) that f:R×l2→l2 is a locally Lipschitz continuous function, that is, for every n∈N, we can find a constant c1(n)>0 satisfying, for all u,v∈l2 with ‖u‖≤n and ‖v‖≤n,
‖f(u)−f(v)‖≤c1(n)‖u−v‖. | (2.10) |
For q∈[2,p) and u∈l2, one can deduce from(2.4), (2.7) and Young's inequality that for all ϖ>0,
ϖ∑j∈N‖σj(t,u)‖2=ϖ∑j∈N∑i∈Zk|δi,jˆσi,j(t,ui)|2≤2ϖ∑j∈N∑i∈Zk|δi,j|2(|φ1,i|2|ui|q+|φ2,i|2)≤2ϖcδ∑i∈Zk(|φ1,i|2|ui|q+|φ2,i|2)≤γ12∑i∈Zk|ui|p+p−qp(pγ12q)−qp−q(2ϖcδ)pp−q∑i∈Zk|φ1,i|2pp−q+2ϖcδ∑i∈Zk|φ2,i|2≤γ12‖u‖pp+p−qp(pγ12q)−qp−q(2ϖcδ)pp−q‖φ1‖2pp−q2pp−q+2ϖcδ‖φ2‖2, | (2.11) |
where γ1 is the same number as in (2.1). From (2.11) and l2⊆lp for p>2, we find that σj(t,u)∈l2 for all u∈l2. Then σj:R×l2→l2 is also well-defined. In addition, it yields from (2.5) and (2.7) that there exists η∈(0,1) such that for q∈[2,p) and u,v∈l2,
∑j∈N∑i∈Zk|δi,jˆσi,j(t,ui)−δi,jˆσi,j(t,vi)|2=∑i∈Zk∑j∈N|δi,j|2|ˆσi,j(t,ui)−ˆσi,j(t,vi)|2=∑i∈Zk∑j∈N|δi,j|2|ˆσ′i,j(ηui+(1−η)vi)|2|ui−vi|2≤cδ∑i∈Zk(|φ3,i||ηui+(1−η)vi|q2−1+|φ4,i|)2|ui−vi|2≤cδ∑i∈Zk(2q−2|φ3,i|2(|ui|q−2+|vi|q−2)+2|φ4,i|2)|ui−vi|2≤cδ∑i∈Zk(2q−2(4q|φ3,i|q+q−2q|ui|q+q−2q|vi|q)+2|φ4,i|2)|ui−vi|2≤cδ(2q−1(‖φ3‖qq+‖u‖q+‖v‖q)+2‖φ4‖2l∞)‖u−v‖2. | (2.12) |
This implies that σj:R×l2→l2 is also locally Lipschitz continuous, more precisely, for every n∈N, one can find a constant c2(n)>0 satisfying, for all u,v∈l2 with ‖u‖≤n and ‖v‖≤n,
∑j∈N‖σj(u)‖2≤c22(n). | (2.13) |
and
∑j∈N‖σj(u)−σj(v)‖2≤c22(n)‖u−v‖2. | (2.14) |
By above notations one is able to rewrite (1.1)–(1.2) as the following system in l2 for t>0 :
du(t)+ν(t)Aku(t)dt+λ(t)u(t)dt=f(t,u(t))dt+g(t)dt+∞∑j=1(hj(t)+σj(t,u(t)))dWj(t), | (2.15) |
with initial condition:
u(0)=u0∈l2, | (2.16) |
in the present article, the solutions of system (2.15)–(2.16) are interpreted in the following sense.
Definition 2.1. Suppose u0∈L2(Ω,l2) is F0-measurable, a continuous l2-valued Ft-adapted stochastic process u is called a solution of equations (2.15) and (2.16) if u∈L2(Ω,C([0,T],l2))∩Lp(Ω,Lp(0,T;lp)) for all T>0, and the following equation holds for all t≥0 and almost all ω∈Ω:
u(t)=u0+∫t0(−ν(s)Aku(s)−λ(s)u(s)+f(s,u(s))+g(s))ds+∞∑j=1∫t0(hj(s)+σj(s,u(s)))dWj(s) in l2. | (2.17) |
Similar to Ref.[20], we can get (2.15) and (2.16) exist global solutions in the sense of Definition 2.1.
In this section, we derive the uniform estimates of solutions of (2.15)–(2.16). These estimates will be used to establish the tightness of a set of probability distributions of u in l2.
We assume that
α(t)=λ(t)−16k|ν(t)|>0. | (3.1) |
Lemma 3.1. Let (2.1)–(2.7) and (3.1) hold. Then the solutions u(t,0,u0) of system (2.15) and (2.16) with initial data u0 at time 0 satisfy, for all t≥0,
E(‖u(t,0,u0)‖2)+∫t0eα_(r−t)E(‖u(r,0,u0)‖pp)dr≤L1(E(‖u0‖2)+∞∑j=1¯‖hj‖2+¯‖g‖2+‖φ1‖2pp−q2pp−q+‖φ2‖2+‖ϕ1‖1), | (3.2) |
where L1>0 is a positive constant which depends on α_,p,q,γ,cδ,t, but indepentent of u0.
Proof. Applying Ito's formula to (2.15) we get
d(‖u(t)‖2)+2ν(t)k∑j=1‖Bju(t)‖2dt+2λ(t)‖u(t)‖2dt=2(f(t,u(t)),u(t))dt+2(g(t),u(t))dt+∞∑j=1‖hj(t)+σ(t,u(t))‖2dt+2∞∑j=1u(t)(hj(t)+σj(t,u(t)))dWj(t). |
This implies
ddtE(‖u(t)‖2)+2ν(t)k∑j=1E(‖Bju(t)‖2)+2λ(t)E(‖u(t)‖2)≤2E(f(t,u(t)),u(t))+2E(g(t),u(t))+2∞∑j=1E(‖hj(t)‖2)+2∞∑j=1E(‖σ(t,u(t))‖2). | (3.3) |
For the second term on the left-hand side of (3.3), we have
2|ν(t)|k∑j=1E(‖Bju(t)‖2)≤8k|ν(t)|E(‖u(t)‖2). | (3.4) |
For the first term on the right-hand side of (3.3), we get from (2.1) that
2E(f(t,u(t)),u(t))≤−2γ1E(‖u(t)‖pp)+2‖ϕ1‖1. | (3.5) |
For the second term on the right-hand side of (3.3), we have
2E(g(t),u(t))≤λ(t)E(‖u(t)‖2)+1λ(t)E(‖g(t)‖2). | (3.6) |
For the last term on the right-hand side of (3.3), we infer from (2.11) with ω=2 that
2∞∑j=1E(‖σj(t,u(t))‖2)≤γ12E(‖u(t)‖pp)+p−qp(pγ12q)−qp−q(4cδ)pp−q‖φ1‖2pp−q2pp−q+4cδ‖φ2‖2. | (3.7) |
By (3.3)–(3.7) we get
ddtE(‖u(t)‖2)+α_E(‖u(t)‖2)+32γ1E(‖u(t)‖pp)≤E(∞∑j=12‖hj(t)‖2+1λ(t)‖g(t)‖2)+C1, | (3.8) |
implies that
ddtE(‖u(t)‖2)+α_E(‖u(t)‖2)+32γ1E(‖u(t)‖pp)≤2∞∑j=1‖¯hj‖2+1λ_‖¯g‖2+C1, | (3.9) |
where C1=p−qp(pγ12q)−qp−q(4cδ)pp−q‖φ1‖2pp−q2pp−q+4cδ‖φ2‖2+2‖ϕ1‖1. Multiplying (3.9) by eα_t and integrating over (0,t) to obtain
E(‖u(t,0,u0)‖2)+32γ1∫t0eα_(r−t)E(‖u(r,0,u0)‖pp)dr≤e−α_tE(‖u0‖2)+C2∫t0eα_(r−t)dr, | (3.10) |
where C2=2∑∞j=1‖¯hj‖2+1λ_‖¯g‖2+C1. This completes the proof.
Lemma 3.2. Let (2.1)–(2.7), and (3.1) be satisfied. Then for compact subset K of l2, one can find a number N0=N0(K)∈N such that the solutions u(t,0,u0) of (2.15) and (2.16) satisfy, for all n≥N0 and t≥0,
E(∑‖i‖≥n|ui(t,0,u0)|2)+∫t0eα_(r−t)E(∑‖i‖≥n|ui(r,0,u0)|p)dr≤ε, | (3.11) |
where u0∈K and ‖i‖:=maxi≤j≤k|ij|.
Proof. Define a smooth function ξ:R→[0,1] such that
ξ(s)=0 for |s|≤1 and ξ(s)=1 for |s|≥2. | (3.12) |
Denote by
ξn=(ξ(‖i‖n))i∈Zk and ξnu=(ξ(‖i‖n)ui)i∈Zk,∀u=(ui)i∈Zk,n∈N. | (3.13) |
Similar notations will also be used for other terms. It follows from (2.15) that
d(ξnu(t))+ν(t)ξnAku(t)dt+λ(t)ξnu(t)dt=ξnf(t,u(t))dt+ξng(t)dt+∞∑j=1(ξnhj(t)+ξnσj(t,u(t)))dWj(t). | (3.14) |
By Ito's formula and (3.14) we have
d‖ξnu(t)‖2+2ν(t)(Ak(u(t)),ξ2nu(t))dt+2λ(t)‖ξnu(t)‖2dt=2(f(t,u(t)),ξ2nu(t))dt+2(g(t),ξ2nu(t))dt+∞∑j=1‖ξnhj(t)+ξnσj(t,u(t))‖2dt+2∞∑j=1(hj(t)+σj(t,u(t)),ξ2nu(t))dWj. | (3.15) |
This yields
ddtE(‖ξnu(t)‖2)+2ν(t)E(Ak(u(t)),ξ2nu(t))+2λ(t)E(‖ξnu(t)‖2)=2E(f(t,u(t)),ξ2nu(t))+2E(g(t),ξ2nu(t))+2∞∑j=1E(‖ξnhj(t)‖2)+2∞∑j=1E(‖ξnσj(t,u(t))‖2)dt. | (3.16) |
For the second term on the left-hand side of (3.16), we have
2ν(t)E(Ak(u(t)),ξ2nu(t))=2ν(t)k∑j=1E(Bju(t),Bj(ξ2nu(t)))=2ν(t)E(k∑j=1∑i∈Zk(ui1,…,ij+1,…,ik−ui)×(ξ2(‖(i1,…,ij+1,…,ik)‖n)u(i1,…,ij+1,…,ik)−ξ2(‖i‖n)ui))=2ν(t)E(k∑j=1∑i∈Zkξ2(‖i‖n)(ui1,…,ij+1,…,ik−ui)2)+2ν(t)E(k∑j=1∑i∈Zk(ξ2(‖(i1,…,ij+1,…,ik)‖n)−ξ2(‖i‖n))×(u(i1,…,ij+1,…,ik)−ui)u(i1,…,ij+1,…,ik)). | (3.17) |
We first deal with the first term on the right-hand side of (3.17). Notice that
2|ν(t)|E(k∑j=1∑i∈Zkξ2(‖i‖n)(ui1,…,ij+1,…,ik−ui)2)=2|ν(t)|E(k∑j=1∑i∈Zk|ξ(‖i‖n)u(i1,…,ij+1,…,ik)−ξ(‖i‖n)ui|2)≤4|ν(t)|E(k∑j=1∑i∈Zk|(ξ(‖i‖n)−ξ(‖(i1,…,ij+1,…,ik)‖n))u(i1,…,ij+1,…,ik)|2)+4|ν(t)|E(k∑j=1∑i∈Zk|ξ(‖(i1,…,ij+1,…,ik)‖n)u(i1,…,ij+1,…,ik)−ξ(‖i‖n)ui|2). | (3.18) |
By the definition of function ξ, there exists a constant C3>0 such that |ξ′(s)|≤C3 for all s∈R. Then the first term on the right-hand side of (3.18) is bounded by
4|ν(t)|E(k∑j=1∑i∈Zk|(ξ(‖i‖n)−ξ(‖(i1,…,ij+1,…,ik)‖n))u(i1,…,ij+1,…,ik)|2)=4|ν(t)|E(k∑j=1∑i∈Zk|ξ(‖i‖n)−ξ(‖(i1,…,ij+1,…,ik)‖n)|2|u(i1,…,ij+1,…,ik)|2)≤4C23n2|ν(t)|E(k∑j=1∑i∈Zk|u(i1,…,ij+1,…,ik)|2)≤4C23kn2|ν(t)|E(‖u‖2). | (3.19) |
By the definition of |Bju|i, the last term on the right-hand side of (3.18) is bounded by
4|ν(t)|E(k∑j=1∑i∈Zk|ξ(‖(i1,…,ij+1,…,ik)‖n)u(i1,…,ij+1,…,ik)−ξ(‖i‖n)ui|2)≤4|ν(t)|E(k∑j=1‖Bj(ξnu(t))‖2)≤16k|ν(t)|E(‖ξnu(t)‖2). | (3.20) |
Then we find from (3.18) to (3.20) that the first term on the right-hand side of (3.17) is bounded by
2|ν(t)|E(k∑j=1∑i∈Zkξ2(‖i‖n)(u(i1,…,ij+1,…,ik)−ui)2)≤16k|ν(t)|E(‖ξnu(t)‖2)+4C23kn2|ν(t)|E(‖u‖2). | (3.21) |
In addition, we find that the last term on the right-hand side of (3.17) can be bounded by
2|ν(t)E(k∑j=1∑i∈Zk(ξ2(‖(i1,…,ij+1,…,ik)‖n)−ξ2(‖i‖n))×(u(i1,…,ij+1,…,ik)−ui)u(i1,…,ij+1,…,ik))|≤2|ν(t)|E(k∑j=1∑i∈Zk|ξ2(‖(i1,…,ij+1,…,ik)‖n)−ξ2(‖i‖n)|×|u(i1,…,ij+1,…,ik)−ui||u(i1,…,ij+1,…,ik)|)≤4|ν(t)|E(k∑j=1∑i∈Zk|ξ(‖(i1,…,ij+1,…,ik)‖n)−ξ(‖i‖n)|×|u(i1,…,ij+1,…,ik)−ui||u(i1,…,ij+1,…,ik)|)≤4C3n|ν(t)|E(k∑j=1∑i∈Zk|u(i1,…,ij+1,…,ik)−ui||u(i1,…,ij+1,…,ik)|)≤8kC3n|ν(t)|E(‖u‖2). | (3.22) |
By (3.21), (3.22) and (3.17), we infer that the second term on the left-hand side of (3.16) satisfied
2|ν(t)E(Ak(u(t)),ξ2nu(t))|≤C4|ν(t)|(1n+1n2)E(‖u‖2)+16k|ν(t)|E(‖ξnu(t)‖2), | (3.23) |
where C4=4kC3(2+C3). For the first term on the right-hand side of (3.16), we find from (2.1) that
2E(f(t,u(t)),ξ2nu(t))≤−2γ1E(∑i∈Zkξ2(‖i‖n)|ui(t)|p)+2E(∑i∈Zkξ2(‖i‖n)|ϕ1,i|)≤−2γ1E(∑i∈Zkξ2(‖i‖n)|ui(t)|p)+2∑‖i‖≥n|ϕ1,i|. | (3.24) |
For the second term on the right-hand side of (3.16), we infer from Young's inequality that
2E(g,ξ2nu(t))≤λ_E(‖ξnu(t)‖2)+1λ_E(∑i∈Zkξ2(‖i‖n)|gi|2)≤λ_E(‖ξnu(t)‖2)+1λ_∑‖i‖≥n|gi|2. | (3.25) |
For the last term on the right-hand side (3.16), we infer from (2.4) and Young's inequality that
2∞∑j=1E(‖ξnσj(t,u(t))‖2)=2∞∑j=1E(∑i∈Zk|ξ(‖i‖n)δi,jˆσi,j(t,ui(t))|2)≤4∞∑j=1E(∑i∈Zkξ2(‖i‖n)|δi,j|2(|φ1,i|2|ui(t)|q+|φ2,i|2))≤4cδE(∑i∈Zkξ2(‖i‖n)(|φ1,i|2|ui(t)|q+|φ2,i|2))≤γ1E(∑i∈Zkξ2(‖i‖n)|ui(t)|p)+p−qp(pγ1q)−qp−q(4cδ)pp−q∑i∈Zkξ2(‖i‖n)|φ1,i|2pp−q+4cδ∑i∈Zkξ2(‖i‖n)|φ2,i|2≤γ1E(∑i∈Zkξ2(‖i‖n)|ui(t)|p)+p−qp(pγ1q)−qp−q(4cδ)pp−q∑‖i‖≥n|φ1,i|2pp−q+4cδ∑‖i‖≥n|φ2,i|2. | (3.26) |
Substituting (3.23)–(3.26) into (3.16) we get
ddtE(‖ξnu(t)‖2)+α_E(‖ξnu(t)‖2)+γ1E(∑i∈Zkξ2(‖i‖n)|ui(t)|p)≤C4|ν|(1n+1n2)E(‖u‖2)+C5(∑‖i‖≥n(¯|gi|2+|φ1,i|2pp−q+|φ2,i|2+|ϕ1,i|)+∑‖i‖≥n∞∑j=1¯|hi,j|2), | (3.27) |
where C5=2+1λ_+p−qp(pγ1q)−qp−q(4cδ)pp−q+4cδ. One can multiply (3.27) by eα_t and integrate over (0,t) in order to obtain
E(‖ξnu(t,0,u0)‖2)+γ1∫t0eα_(r−t)E(∑i∈Zkξ2(‖i‖n)|ui(r,0,u0)|p)dr≤e−α_tE(‖ξnu0‖2)+C4|ν|(1n+1n2)∫t0eα_(r−t)E(‖u(r,0,u0)‖2)dr+C5α_(∑‖i‖≥n(¯|gi|2+|φ1,i|2pp−q+|φ2,i|2+|ϕ1,i|)+∑‖i‖≥n∞∑j=1¯|hi,j|2). | (3.28) |
Since K is a compact subset of l2 we infer from (3.1) that
limn→∞supu0∈Ksupt≥0e−α_tE(‖ξnu0‖2)≤limn→∞supu0∈KE(∑‖i‖≥n|u0,i|2)=0. | (3.29) |
By Lemma 3.1, we find that for all u0∈K and t≥0, as n→∞,
(1n+1n2)∫t0eα_(r−t)E(‖u(r,0,u0)‖2)dr≤L1α_(1n+1n2)(E(‖u0‖2)+∞∑j=1¯‖hj‖2+¯‖g‖2+‖φ1‖2pp−q2pp−q+‖φ2‖2+‖ϕ1‖1)≤L1α_(1n+1n2)(C6+∞∑j=1¯‖hj‖2+¯‖g‖2+‖φ1‖2pp−q2pp−q+‖φ2‖2+‖ϕ1‖1)→0, | (3.30) |
where L1 is the same number of (3.1) and C6>0 is a constant depending only on u0.By φ1∈l2pp−q,φ2∈l2,ϕ1∈l1, (2.6) and (3.1), we infer that
∑‖i‖≥n(¯|gi|2+|φ1,i|2pp−q+|φ2,i|2+|ϕ1,i|)+∑‖i‖≥n∞∑j=1¯|hi,j|2→0 as n→∞. | (3.31) |
It follows from (3.28) to (3.31) that as n→∞,
supu0∈Ksupt≥0(E(‖ξnu(t,0,u0)‖2)+∫t0eα_(r−t)E(∑i∈Zkξ2(‖i‖n)|ui(r,0,u0)|p)dr)→0. | (3.32) |
Then for every ε>0 we can find a number N0=N0(K)∈N satisfying, for all n≥N0 and t≥0,
(E(∑‖i‖≥2n|ui(t,0,u0)|2)+∫t0eα_(r−t)E(∑‖i‖≥2n|ui(t,0,u0)|p)dr)≤(E(‖ξnu(t,0,u0)‖2)+∫t0eα_(r−t)E(∑i∈Zkξ2(‖i‖n)|ui(t,0,u0)|p)dr)≤ε, | (3.33) |
uniformly for u0∈K and t≥0. This concludes the proof.
In the sequel, we use L(u(t,0,u0)) to denote the probability distribution of the solution u(t,0,u0) of (2.15)–(2.16) which has initial condition u0 at initial time 0. Then we have the following tightness of a family of distributions of solutions.
Lemma 4.1. Suppose (2.1)–(2.7) and (3.1) hold. Then the family {L(u(t,0,u0)):t≥0} of the distributions ofthe solutions of (2.15)–(2.16) is tight on l2.
Proof. For simplicity, we will write the solution u(t,0,u0) as u(t) from now on. It follows from Lemma 3.1 that there exists a constant c1>0 such that
E(‖u(t)‖2)≤c1,for allt≥0. | (4.1) |
By Chebyshev's inequality, we get from (4.1) that for all t≥0,
P(‖u(t)‖2≥R)≤c1R2→0asR→∞. |
Hence for every ϵ>0, there exists R1=R1(ϵ)>0 such that for all t≥0,
P{‖u(t)‖2≥R1}≤12ϵ. | (4.2) |
By Lemma 3.2, we infer that for each ϵ>0 and m∈N, there exists an integer nm=nm(ϵ,m) such that for all t≥0,
E(∑|i|>nm|ui(t)|2)<ϵ22m+2, |
and hence for all t≥0 and m∈N,
P({∑|i|>nm|ui(r)|2≥12m})≤2mE(∑|i|>nm|ui(r)|2)<ϵ2m+2. | (4.3) |
It follows from (4.3) for all t≥0,
P(∞∪m=1{∑|i|>nm|ui(t)|2≥12m})≤∞∑m=1ϵ2m+2≤14ϵ, |
which shows that for all t≥0,
P({∑|i|>nm|ui(t)|2≤12mfor allm∈N})>1−ϵ2. | (4.4) |
Given ϵ>0, set
Y1,ϵ={v∈l2:‖v‖≤R1(ϵ)}, | (4.5) |
Y2,ϵ={v∈l2:∑|i|>nm|vi(r)|2≤12mfor allm∈N}, | (4.6) |
and
Yϵ=Y1,ϵ∩Y2,ϵ. | (4.7) |
By (4.2) and (4.4) we get, for all t≥0,
P({u(t)∈Yϵ})>1−ϵ. | (4.8) |
Now, we show the precompactness of {v:v∈Yϵ} in l2. Given κ>0, choose an integer m0=m0(κ)∈N such that 2m0>8κ2. Then by (4.6) we obtain
∑|i|>nm0|vi|2≤12m0<κ28,∀v∈Yϵ. | (4.9) |
On the other hand, by (4.5) we see that the set {(vi)|i|≤m0:v∈Yϵ} is bounded in the finite-dimensional space R2m0+1 and hence precompact. Consequently, {v:v∈Yϵ} has a finite open cover of balls with radius κ2, which along with (4.9) implies that the set {v:v∈Yϵ} has a finite open cover of balls with radius κ in l2. Since κ>0 is arbitrary, we find that the set {v:v∈Yϵ} is precompact in l2. This completes the proof.
If ϕ:l2→R is a bounded Borel function, then for 0≤r≤t and u0∈l2, we set
(pr,tϕ)(u0)=E(ϕ(u(t,r,u0))) |
and
p(r,u0;t,Γ)=(pr,t1Γ)(u0), |
where Γ∈B(l2) and 1Γ is the characteristic function of Γ. The operators ps,t with 0≤s≤t are called the transition operators for the solutions of (2.15)–(2.16). Recall that a probability measure ν on l2 is periodic for (2.15)–(2.16) if
∫l2(p0,t+Tϕ)(u0)dν(u0)=∫l2(p0,tϕ)(u0)dν(u0),∀t≥0. |
Lemma 4.2. [21]Let ϱ(ψ,ω) be a scalar bounded measurable randomfunction of ψ, independent of Fs. Let ς be anFs-measurable random variable. Then
E(ϱ(ς,ω)|Fs)=E(ϱ(ς,ω)). |
The transition operators {pr,t}0≤r≤t have the following properties.
Lemma 4.3. Assume that (2.1)–(2.7) and (3.1) hold. Then:
(i) {pr,t}0≤r≤t is Feller; that is, for every bounded andcontinuous ϕ:l2→R, the function pr,tϕ:l2→Ris also bounded and continuous for all 0≤r≤t.
(ii) The family {pr,t}0≤r≤t is T-periodic; that is, for all 0≤r≤t,
p(r,u0;t,⋅)=p(r+T,u0;t+T,⋅),∀u0∈l2. |
(iii) {u(t,0,u0)}t≥0 is a l2-valued Markov process.
Finally, we present our main result on the existence of periodic measures for problem (2.15)–(2.16).
Theorem 4.4. Assume that (2.1)–(2.7) and (3.1) hold. Then problem (2.15)–(2.16) has a periodic measure on l2.
Proof. We apply Krylov-Bogolyubov's method to prove the existence of periodic measures of (2.15)–(2.16), define a probability measure μn by
μn=1nn∑l=1p(0,0;lT,⋅). | (4.10) |
By Lemma 4.1 we see the sequence {μn}∞n=1 is tight on l2, and hence there exists a probability measure μ on l2 such that, up to a subsequence,
μn→μ,as n→∞. | (4.11) |
By (4.10)–(4.11) and Lemma 4.3, we infer that for every t≥0 and every bounded and continuous function ϕ:l2→R,
∫l2(p0,tϕ)(u0)dμ(u0)=∫l2(∫l2ϕ(y)p(0,u0;t,dy))dμ(u0)=limn→∞1nn∑l=1∫l2(∫l2ϕ(y)p(0,u0;t,dy))p(0,0;lT,du0)=limn→∞1nn∑l=1∫l2(∫l2ϕ(y)p(kT,u0;t+lT,dy))p(0,0;kT,du0)=limn→∞1nn∑l=1∫l2ϕ(y)p(0,0;t+lT,dy)=limn→∞1nn∑l=1∫l2ϕ(y)p(0,0;t+lT+T,dy)=limn→∞1nn∑k=1∫l2(∫l2ϕ(y)p(0,u0;t+T,dy))p(0,0;lT,du0)=∫l2(∫l2ϕ(y)p(0,u0;t+T,dy))dμ(u0)=∫l2(p0,t+Tϕ)(u0)dμ(u0), | (4.12) |
which shows that μ is a periodic measure of (2.15)–(2.16), as desired.
The author declares there is no conflict of interest.
[1] | P. F. Antonietti, S. Giani, P. Houston, hp-version composite discontinuous Galerkin methods for elliptic problems on complicated domains, SIAM J. Sci. Comput., 35 (2013), A1417–A1439. https://doi.org/10.1137/120877246 |
[2] | D. N. Arnold, D. Boffi, R. S. Falk, Quadrilateral H(div) finite elements, SIAM J. Numer. Anal., 42 (2005), 2429–2451. https://doi.org/10.1137/S0036142903431924 |
[3] | R. E. Bank, J. Xu, Asymptotically exact a posteriori error estimators, Part I: Grids with superconvergence, SIAM J. Numer. Anal., 41 (2003), 2294–2312. https://doi.org/10.1137/S003614290139874X |
[4] | F. Bassi, L. Botti, A. Colombo, D. Di Pietro, P. Tesini, On the flexibility of agglomeration based physical space discontinuous Galerkin discretizations, J. Comput. Phys., 231 (2012), 45–65. https://doi.org/10.1016/j.jcp.2011.08.018 |
[5] | L. Beirão da Veiga, F. Brezzi, A. Cangiani, G. Manzini, L. D. Marini, A. Russo, Basic principles of virtual element method, Math. Mod. Meth. Appl. Sci., 23 (2013), 199–214. https://doi.org/10.1142/S0218202512500492 |
[6] | P. B. Bochev, M. D. Gunzburger, Accuracy of least-squares methods for the Navier-Stokes equations, Comput. Fluids, 22 (1993), 549–563. https://doi.org/10.1016/0045-7930(93)90025-5 |
[7] | P. B. Bochev, M. D. Gunzburger, Analysis of least-squares finite element methods for the Stokes equations, Math. Comp., 63 (1994), 479–506. |
[8] | P. B. Bochev, M. D. Gunzburger, A locally conservative least-squares method for Darcy flows, Commum. Numer. Meth. Eng., 24 (2008), 97–110. https://doi.org/10.1002/cnm.957 |
[9] | J. H. Brandts, Superconvergence and a posteriori error estimation for triangular mixed finite elements, Numer. Math., 68 (1994), 311–324. https://doi.org/10.1007/s002110050064 |
[10] |
S. C. Brenner, Poincaré-Friedrichs inequalities for piecewise H1 functions, SIAM J. Numer. Anal., 41 (2003), 306–324. https://doi.org/10.1137/S0036142902401311 doi: 10.1137/S0036142902401311
![]() |
[11] | Z. Cai, V. Carey, J. Ku, E. J. Park, Asymptotically exact a posteriori error estimators for first-order div least-squares methods in local and global L2 norm, Comput. Math. Appl., 70 (2015), 648–659. https://doi.org/10.1016/j.camwa.2015.05.010 |
[12] |
Z. Cai, J. Ku, The L2 norm error estimates for the div least-squares method, SIAM J. Numer. Anal., 44 (2006), 1721–1734. https://doi.org/10.1137/050636504 doi: 10.1137/050636504
![]() |
[13] |
Z. Cai, R. Lazarov, T. Manteuffel, S. McCormick, First order system least-squares for second-order partial differential equations: Part I, SIAM J. Numer. Anal., 31 (1994), 1785–1799. https://doi.org/10.1137/0731091 doi: 10.1137/0731091
![]() |
[14] |
Z. Cai, G. Starke, Least-squares methods for linear elasticity, SIAM J. Numer. Anal., 42 (2004), 826–842. https://doi.org/10.1137/S0036142902418357 doi: 10.1137/S0036142902418357
![]() |
[15] |
A. Cangiani, E. H. Georgoulis, T. Pryer, O. J. Sutton, A posteriori error estimates for the virtual element method, Numer. Math., 137 (2017), 857–893. https://doi.org/10.1007/s00211-017-0891-9 doi: 10.1007/s00211-017-0891-9
![]() |
[16] | A. Cangiani, Z. Dong, E. H. Georgoulis, P. Houston, hp-version discontinuous Galerkin methods on polytopic meshes, Springer, 2017. https://doi.org/10.1007/978-3-319-67673-9 |
[17] |
C. Carstensen, E. J. Park, Convergence and optimality of adaptive least squares finite element methods, SIAM. J. Numer. Anal., 53 (2015), 43–62. https://doi.org/10.1137/130949634 doi: 10.1137/130949634
![]() |
[18] |
C. Carstensen, E. J. Park, P. Bringmann, Convergence of natural adaptive least squares finite element methods, Numer. Math., 136 (2017), 1097–1115. https://doi.org/10.1007/s00211-017-0866-x doi: 10.1007/s00211-017-0866-x
![]() |
[19] |
C. L. Chang, A least-squares finite element method for the Helmholtz equation, Comput. Meth. Appl. Mech. Eng., 83 (1990), 1–7. https://doi.org/10.1016/0045-7825(90)90121-2 doi: 10.1016/0045-7825(90)90121-2
![]() |
[20] |
E. T. Chung, B. Engquist, Optimal discontinuous Galerkin methods for wave propagation, SIAM J. Numer. Anal., 44 (2006), 2131–2158. https://doi.org/10.1137/050641193 doi: 10.1137/050641193
![]() |
[21] |
E. T. Chung, B. Engquist, Optimal discontinuous Galerkin methods for the acoustic wave equation in higher dimensions, SIAM J. Numer. Anal., 47 (2009), 3820–3848. https://doi.org/10.1137/080729062 doi: 10.1137/080729062
![]() |
[22] |
E. T. Chung, E. J. Park, L. Zhao, Guaranteed a posteriori error estimates for a staggered discontinuous Galerkin method, J. Sci. Comput., 75 (2018), 1079–1101. https://doi.org/10.1007/s10915-017-0575-8 doi: 10.1007/s10915-017-0575-8
![]() |
[23] | P. G. Ciarlet, The finite element method for elliptic problems, North-Holland Publishing Company, 1978. |
[24] | B. Cockburn, J. Gopalakrishnan, R. Lazarov, Unified hybridization of discontinuous Galerkin, mixed, and continuous Galerkin methods for second order elliptic problems, SIAM J. Numer. Anal., 47 (2009), 1319–1365. https://doi.org/10.1137/070706616 |
[25] | D. A. Di Pietro, A. Ern, S. Lemaire, An arbitrary-order and compact-stencil discretization of diffusion on general meshes based on local reconstruction operators, Comput. Meth. Appl. Math., 14 (2014), 461–472. https://doi.org/10.1515/cmam-2014-0018 |
[26] |
R. E. Ewing, R. D. Lazarov, J. Wang, Superconvergence of the velocity along the Gauss lines in mixed finite element methods, SIAM J. Numer. Anal., 28 (1991), 1015–1029. https://doi.org/10.1137/0728054 doi: 10.1137/0728054
![]() |
[27] |
R. E. Ewing, M. M. Liu, J. Wang, Superconvergence of mixed finite element approximations over quadrilaterals, SIAM J. Numer. Anal., 36 (1999), 772–787. https://doi.org/10.1137/S0036142997322801 doi: 10.1137/S0036142997322801
![]() |
[28] |
Y. Huang, J. Xu, Superconvergence of quadratic finite elements on mildly structured grids, Math. Comp., 77 (2008), 1253–1268. https://doi.org/10.1090/S0025-5718-08-02051-6 doi: 10.1090/S0025-5718-08-02051-6
![]() |
[29] |
J. Jou, J. L. Liu, A posteriori least-squares finite element error analysis for the Navier-Stokes equations, Numer. Funct. Anal. Optim., 24 (2003), 67–74. https://doi.org/10.1081/NFA-120020245 doi: 10.1081/NFA-120020245
![]() |
[30] |
D. Kim, L. Zhao, E. J. Park, Staggered DG methods for the pseudostress-velocity formulation of the Stokes equations on general meshes, SIAM J. Sci. Comput., 42 (2020), A2537–A2560. https://doi.org/10.1137/20M1322170 doi: 10.1137/20M1322170
![]() |
[31] | D. Kim, L. Zhao, E. J. Park, Review and implementation of staggered DG methods on polygonal meshes, J. Korean Soc. Ind. Appl. Math., 25 (2021), 66–81. |
[32] |
J. Ku, E. J. Park, A posteriori error estimators for the first-order least-squares finite element method, J. Comput. Appl. Math., 235 (2010), 293–300. https://doi.org/10.1016/j.cam.2010.06.004 doi: 10.1016/j.cam.2010.06.004
![]() |
[33] |
Y. W. Li, Global superconvergence of the lowest-order mixed finite element on mildly structured meshes, SIAM J. Numer. Anal., 56 (2018), 792–815. https://doi.org/10.1137/17M112587X doi: 10.1137/17M112587X
![]() |
[34] |
R. Rannacher, A posteriori error estimation in least-squares stabilized finite element schemes, Comput. Meth. Appl. Mech. Eng., 166 (1998), 99–114. https://doi.org/10.1016/S0045-7825(98)00085-1 doi: 10.1016/S0045-7825(98)00085-1
![]() |
[35] |
J. Wang, Superconvergence and extrapolation for mixed finite element methods on rectangular domains, Math. Comp., 56 (1991), 477–503. https://doi.org/10.1090/S0025-5718-1991-1068807-0 doi: 10.1090/S0025-5718-1991-1068807-0
![]() |
[36] | J. Wang, X. Ye, A weak Galerkin mixed finite element method for second order elliptic problems, Math. Comp., 83 (2014), 2101–2126. |
[37] |
J. Xu, Z. Zhang, Analysis of recovery type a posteriori error estimators for mildly structured grids, Math. Comp., 73 (2003), 1139–1152. https://doi.org/10.1090/S0025-5718-03-01600-4 doi: 10.1090/S0025-5718-03-01600-4
![]() |
[38] |
L. Zhao, E. J. Park, Fully computable bounds for a staggered discontinuous Galerkin method for the Stokes equations, Comput. Math. Appl., 75 (2018), 4115–4134. https://doi.org/10.1016/j.camwa.2018.03.018 doi: 10.1016/j.camwa.2018.03.018
![]() |
[39] |
L. Zhao, E. J. Park, A staggered discontinuous Galerkin method of minimal dimension on quadrilateral and polygonal meshes, SIAM J. Sci. Comput., 40 (2018), 2543–2567. https://doi.org/10.1137/17M1159385 doi: 10.1137/17M1159385
![]() |
[40] | L. Zhao, E. J. Park, D. W. Shin, A staggered discontinuous Galerkin method for the Stokes equations on general meshes, Comput. Meth. Appl. Mech. Eng., 345 (2019), 854–875. |
[41] |
L. Zhao, E. T. Chung, E. J. Park, G. Zhou, Staggered DG method for coupling of the Stokes and Darcy-Forchheimer problems, SIAM J. Numer. Anal., 59 (2021), 1–31. https://doi.org/10.1137/19M1268525 doi: 10.1137/19M1268525
![]() |
[42] |
L. Zhao, E. J. Park, A new hybrid staggered discontinuous Galerkin method on general meshes, J. Sci. Comput., 82 (2020), 12. https://doi.org/10.1007/s10915-019-01119-6 doi: 10.1007/s10915-019-01119-6
![]() |
[43] |
L. Zhao, E. J. Park, A staggered cell-centered DG method for linear elasticity on polygonal meshes, SIAM J. Sci. Comput., 42 (2020), A2158–A2181. https://doi.org/10.1137/19M1278016 doi: 10.1137/19M1278016
![]() |
[44] |
L. Zhao, E. J. Park, A staggered discontinuous Galerkin method for quasi-linear second order elliptic problems of nonmonotone type, Comput. Meth. Appl. Math., 22 (2022), 729–750. https://doi.org/10.1515/cmam-2022-0081 doi: 10.1515/cmam-2022-0081
![]() |
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