Mathematical modeling plays a crucial role in understanding and combating infectious diseases, offering predictive insights into disease spread and the impact of vaccination strategies. This paper explored the significance of mathematical modeling in epidemic control efforts, focusing on the interplay between vaccination strategies, disease transmission rates, and population immunity. To facilitate meaningful comparisons of vaccination strategies, we maintained a consistent framework by fixing the vaccination capacity to vary from 10 to 100% of the total population. As an example, at a 50% vaccination capacity, the pulse strategy averted approximately 45.61% of deaths, while continuous and hybrid strategies averted around 45.18 and 45.69%, respectively. Sensitivity analysis further indicated that continuous vaccination has a more direct impact on reducing the basic reproduction number R0 compared to pulse vaccination. By analyzing key parameters such as R0, pulse vaccination coefficients, and continuous vaccination parameters, the study underscores the value of mathematical modeling in shaping public health policies and guiding decision-making during disease outbreaks.
Citation: Diana Bolatova, Shirali Kadyrov, Ardak Kashkynbayev. Mathematical modeling of infectious diseases and the impact of vaccination strategies[J]. Mathematical Biosciences and Engineering, 2024, 21(9): 7103-7123. doi: 10.3934/mbe.2024314
[1] | Hiroshi Nishiura . Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 49-64. doi: 10.3934/mbe.2011.8.49 |
[2] | Qingling Zeng, Kamran Khan, Jianhong Wu, Huaiping Zhu . The utility of preemptive mass influenza vaccination in controlling a SARS outbreak during flu season. Mathematical Biosciences and Engineering, 2007, 4(4): 739-754. doi: 10.3934/mbe.2007.4.739 |
[3] | Kasia A. Pawelek, Anne Oeldorf-Hirsch, Libin Rong . Modeling the impact of twitter on influenza epidemics. Mathematical Biosciences and Engineering, 2014, 11(6): 1337-1356. doi: 10.3934/mbe.2014.11.1337 |
[4] | Xiaomeng Wang, Xue Wang, Xinzhu Guan, Yun Xu, Kangwei Xu, Qiang Gao, Rong Cai, Yongli Cai . The impact of ambient air pollution on an influenza model with partial immunity and vaccination. Mathematical Biosciences and Engineering, 2023, 20(6): 10284-10303. doi: 10.3934/mbe.2023451 |
[5] | Boqiang Chen, Zhizhou Zhu, Qiong Li, Daihai He . Resurgence of different influenza types in China and the US in 2021. Mathematical Biosciences and Engineering, 2023, 20(4): 6327-6333. doi: 10.3934/mbe.2023273 |
[6] | Eunha Shim . Prioritization of delayed vaccination for pandemic influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 95-112. doi: 10.3934/mbe.2011.8.95 |
[7] | Fangyuan Chen, Rong Yuan . Dynamic behavior of swine influenza transmission during the breed-slaughter process. Mathematical Biosciences and Engineering, 2020, 17(5): 5849-5863. doi: 10.3934/mbe.2020312 |
[8] | Dennis L. Chao, Dobromir T. Dimitrov . Seasonality and the effectiveness of mass vaccination. Mathematical Biosciences and Engineering, 2016, 13(2): 249-259. doi: 10.3934/mbe.2015001 |
[9] | Junyuan Yang, Guoqiang Wang, Shuo Zhang . Impact of household quarantine on SARS-Cov-2 infection in mainland China: A mean-field modelling approach. Mathematical Biosciences and Engineering, 2020, 17(5): 4500-4512. doi: 10.3934/mbe.2020248 |
[10] | Sherry Towers, Katia Vogt Geisse, Chia-Chun Tsai, Qing Han, Zhilan Feng . The impact of school closures on pandemic influenza: Assessing potential repercussions using a seasonal SIR model. Mathematical Biosciences and Engineering, 2012, 9(2): 413-430. doi: 10.3934/mbe.2012.9.413 |
Mathematical modeling plays a crucial role in understanding and combating infectious diseases, offering predictive insights into disease spread and the impact of vaccination strategies. This paper explored the significance of mathematical modeling in epidemic control efforts, focusing on the interplay between vaccination strategies, disease transmission rates, and population immunity. To facilitate meaningful comparisons of vaccination strategies, we maintained a consistent framework by fixing the vaccination capacity to vary from 10 to 100% of the total population. As an example, at a 50% vaccination capacity, the pulse strategy averted approximately 45.61% of deaths, while continuous and hybrid strategies averted around 45.18 and 45.69%, respectively. Sensitivity analysis further indicated that continuous vaccination has a more direct impact on reducing the basic reproduction number R0 compared to pulse vaccination. By analyzing key parameters such as R0, pulse vaccination coefficients, and continuous vaccination parameters, the study underscores the value of mathematical modeling in shaping public health policies and guiding decision-making during disease outbreaks.
Stochastic homogenization is a subject broadly studied starting from '80 since the seminal papers by Kozlov [11] and Papanicolaou-Varadhan [18] who studied boundary value problems for second order linear PDEs. We prove here an abstract homogenization result for the graph of a random maximal monotone operator
v(x,ω)∈αε(x,ω,u(x,ω)), |
where
αε(x,ω,⋅):=α(Tx/εω,⋅). | (1) |
The aim of this paper is to extend existing results where
The outline of the proof is the following: Let
Under which assumptions can we conclude that y=Ax? |
A classical answer (see, e.g., [3]) is: If we can produce an auxiliary sequence of points on the graph of
(ξn,ηn)∈X×X′ such that ηn=Anξn, (ξn,ηn)⇀(ξ,η) and η=Aξ, | (2) |
then, denoting by
⟨yn−ηn,xn−ξn⟩≥0. |
In order to pass to the limit as
lim supn→∞⟨gn,fn⟩≤⟨g,f⟩∀(fn,gn)⇀(f,g) in X×X′, | (3) |
which, together with the weak convergence of
⟨y−η,x−ξ⟩≥0. |
By maximal monotonicity of
1. Existence and weak compactness of solutions
2. A condition for the convergence of the duality pairing (3);
3. Existence of a recovery sequence (2) for all points in the limit graph.
The first step depends on the well-posedness of the application; the second step is ensured, e.g., by compensated compactness (in the sense of Murat-Tartar [15,23]), and, like the first one, it depends on the character of the differential operators that appear in the application, rather than on the homogenization procedure. In the present paper we focus on the third step: in the context of stochastic homogenization, we prove that the scale integration/disintegration idea introduced by Visintin [25], combined with Birkhoff's ergodic theorem (Theorem 2.4) yields the desired recovery sequence. We obtain an explicit formula for the limit operator
α a)⟶ f b)⟶ f0 c)⟶ α0, |
where a) the random operator
In Section 2.1 we review the properties of maximal monotone operators and their variational formulation due to Fitzpatrick. In Section 2.2 we recall the basis of ergodic theory that we need in order to state our first main tool: Birkhoff's Ergodic Theorem. Section 3 is devoted to the translation to the stochastic setting of Visintin's scale integration-disintegration theory, which paves the way to our main result, Theorem 3.8. The applications we provide in the last section are: Ohmic electric conduction with Hall effect (Section 4.1), and nonlinear elasticity, (Section 4.2).
We use the notation
In this section we summarize the variational representation of maximal monotone operators introduced in [8]. Further details and proofs of the statements can be found, e.g., in [27]. Let
Gα:={(x,y)∈B×B′:y∈α(x)} |
be its graph. (We will equivalently write
(x,y)∈Gα⇒⟨y−y0,x−x0⟩≥0,∀(x0,y0)∈Gα | (4) |
and strictly monotone if there is
(x,y)∈Gα⇒⟨y−y0,x−x0⟩≥θ‖x−x0‖2,∀(x0,y0)∈Gα. | (5) |
We denote by
x∈α−1(y)⇔y∈α(x). |
The monotone operator
⟨y−y0,x−x0⟩≥0∀(x0,y0)∈Gα⇔(x,y)∈Gα. |
An operator
fα(x,y):=⟨y,x⟩+sup{⟨y−y0,x0−x⟩:(x0,y0)∈Gα}=sup{⟨y,x0⟩+⟨y0,x⟩−⟨y0,x0⟩:(x0,y0)∈Gα}. |
As a supremum of a family of linear functions, the Fitzpatrick function
Lemma 2.1. An operator
(x,y)∈Gα⇒fα(x,y)=⟨y,x⟩, |
while
{fα(x,y)≥⟨y,x⟩ ∀(x,y)∈B×B′fα(x,y)=⟨y,x⟩⟺(x,y)∈Gα. |
In the case
1. Let
fα(x,y)=(y−b+ax)24a+bx. |
2. Let
α(x)={1if x>0,[0,1]if x=0,−1if x<0. |
Then
fα(x,y)={|x|if |y|≤1,+∞if |y|>1. |
and in both cases
We define
f(x,y)≥⟨y,x⟩∀(x,y)∈B×B′. |
We call
(x,y)∈Gαf⇔f(x,y)=⟨y,x⟩. | (6) |
A crucial point is whether
Lemma 2.2. Let
(i) the operator
(ii) the class of maximal monotone operators is strictly contained in the class of operators representable by functions in
Proof. (ⅰ) If
g(P1+P22)−g(P1)+g(P2)2=14(⟨y1+y2,x1+x2⟩)−12(⟨y1,x1⟩+⟨y2,x2⟩)=14(⟨y1,x2⟩+⟨y2,x1⟩−⟨y1,x1⟩−⟨y2,x2⟩)=−14(⟨y2−y1,x2−x1⟩)>0. |
Since
f(P1+P22)>f(P1)+f(P2)2, |
which contradicts the convexity of
(ⅱ) Maximal monotone operators are representable by Lemma 2.1. To see that the inclusion is strict, assume that
h(x,y)=max{c,f(x,y)} |
clearly belongs to
h(x0,y0)≥c>f(x0,y0)=⟨y0,x0⟩, |
and thus
Remark 1. When
φ(x)+φ∗(y)≥⟨y,x⟩∀(x,y)∈B×B′, |
y∈α(x)⇔φ(x)+φ∗(y)=⟨y,x⟩. |
Thus, Fitzpatrick's representative function
fα(x,y)=(x+y)24≠x22+y22=φ(x)+φ∗(y). |
We need to introduce also parameter-dependent operators. For any measurable space
g−1(R):={x∈X:g(x)∩R≠∅} |
is measurable.
Let
α is B(B)⊗A-measurable, | (7) |
α(x,ω) is closed for any x∈B and for μ-a.e. ω∈Ω, | (8) |
α(⋅,ω) is (maximal) monotone for μ-a.e. ω∈Ω. | (9) |
If
(a)
(b)
(c)
As above,
y∈α(x,ω) ⇔ f(x,y,ω)=⟨y,x⟩∀(x,y)∈B×B′,for μ-a.e. ω∈Ω. | (10) |
Precisely, any measurable representative function
In this subsection we review the basic notions and results of stochastic analysis that we need in Section 3. For more details see [10,Chapter 7]. Let
(a)
(b) for every
μ(TxE)=μ(E) | (11) |
(c) for any measurable function
˜f(x,ω)=f(Txω) |
is measurable.
Given an
E(f):=∫Ωfdμ. |
In the context of stochastic homogenization, it is useful to provide an orthogonal decomposition of
∫(vi∂φ∂xj−vj∂φ∂xi)dx=0, ∀i,j=1,…,n,∀φ∈D(Rn) |
and we say that
n∑i=1∫vi∂φ∂xidx=0, ∀φ∈D(Rn). |
Next we consider a vector field on
Lemma 2.3. Define the spaces
Vppot(Ω;Rn):={f∈Lppot(Ω;Rn):E(f)=0},Vpsol(Ω;Rn):={f∈Lpsol(Ω;Rn):E(f)=0}. |
The spaces
E(u⋅v)=E(u)⋅E(v) | (12) |
and the relations
(Vpsol(Ω;Rn))⊥=Vp′pot(Ω;Rn)⊕Rn,(Vppot(Ω;Rn))⊥=Vp′sol(Ω;Rn)⊕Rn |
hold in the sense of duality pairing between the spaces
One of the most important results regarding stochastic homogenization is Birk-hoff's Ergodic Theorem. We report the statement given in [10,Theorem 7.2].
Theorem 2.4. (Birkhoff's Ergodic Theorem) Let
E(f)=limε→01|K|∫Kf(Tx/εω)dx |
for
Remark 2. Birkhoff's theorem implies that
limε→01|K|∫K˜fε(x,ω)dx=E(f). |
Since this holds for every measurable bounded set
˜fε⇀E(f) weakly in Lploc(Rn;Rm) for μ-a.e. ω∈Ω. | (13) |
In what follows, the dynamical system
Let be given a probability space
We rephrase here Visintin's scale integration/disintegration [25,26] to the stochastic homogenization setting.
Remark 3. While most of this subsection's statements are Visintin's results written in a different notation, some others contain a small, but original contribution. Namely: Lemma 3.1 can be found in [26,Lemma 4.1], where the assumption of boundedness for
For every fixed
f(ξ,η,ω)≥c(|ξ|p+|η|p′)+k(ω). | (14) |
We define the homogenised representation
f0(ξ,η):=inf{∫Ωf(ξ+v(ω),η+u(ω),ω)dμ:u∈Vppot(Ω;Rn),v∈Vp′sol(Ω;Rn)}. | (15) |
Lemma 3.1. Let
1i.e., for all
h(x):=infy∈Kg(x,y) |
is weakly lower semicontinuous and coercive. Moreover, if
Proof. Let
lim infj→+∞h(xj)≥h(x). | (16) |
Let
ℓ:=lim infj→+∞h(xj). |
If
h(xj)=infy∈Kg(xj,y)≥g(xj,yj)−ε. | (17) |
Therefore
g(xj,yj)≤2ℓ+ε∀j∈N. |
By the coercivity assumption on
lim infk→+∞h(xjk)≥lim infk→+∞g(xjk,yjk)−ε≥g(x,y)−ε≥h(x)−ε. | (18) |
By arbitrariness of
h(λx1+(1−λ)x2)≤g(λx1+(1−λ)x2,λy1+(1−λ)y2)≤λg(x1,y1)+(1−λ)g(x2,y2). |
Passing to the infimum with respect to
h(λx1+(1−λ)x2)≤λh(x1)+(1−λ)h(x2). |
Regarding the coercivity of
Bt:={x∈X:h(x)≤t},At:={x∈X:g(x,y)≤t, for some y∈K}. |
Let
In the proof of Proposition 1 we need the following estimate
Lemma 3.2. For all
∫Ω|ξ+u(ω)|pdμ≥C∫Ω|ξ|p+|u(ω)|pdμ |
for all
Proof. Consider the operator
Φ:Lp(Ω;Rn)→Lp(Ω;Rn)×Lp(Ω;Rn)u↦(E(u),u−E(u)). |
Clearly,
∫Ω|E(u)|pdμ+∫Ω|u(ω)−E(u)|pdμ≤(‖E(u)‖Lp+‖u−E(u)‖Lp)p≤2p/2(‖E(u)‖2Lp+‖u−E(u)‖2Lp)p/2=2p/2‖Φ(u)‖pLp×Lp≤C‖u‖pLp=C∫Ω|u(ω)|pdμ. |
Apply now the last inequality to
∫Ω|ξ|p+|˜u(ω)|pdμ≤C∫Ω|ξ+˜u(ω)|pdμ. |
Proposition 1. For all
f0(ξ,η)≥ξ⋅η∀(ξ,η)∈Rn×Rn. | (19) |
Proof. Let
Fξ,η(u,v):=∫Ωf(ξ+v(ω),η+u(ω),ω)dμ. |
We prove that the problem
Fξ,η(u,v)≤lim infh→∞Fξ,η(uh,vh)=infKFξ,η. |
This concludes the first part of the statement. We now want to show that
Fξ,η(u,v)≥c∫Ω|ξ+v(ω)|p+|η+u(ω)|p′+k(ω)dμ≥C∫Ω|ξ|p+|u(ω)|p+|η|p′+|v(ω)|p′dμ+E(k)≥C(|ξ|p+‖u‖pLp(Ω)+|η|p′+‖v‖p′Lp′(Ω))−‖k‖L1(Ω). |
Thus, for any
{(ξ,η,(u,v))∈Rn×Rn×K:Fξ,η(u,v)≤M} |
is bounded in
f0(ξ,η)=∫Ωf(ξ+˜u(ω),η+˜v(ω),ω)dμ≥∫Ω(ξ+˜u(ω))⋅(η+˜v(ω))dμ=E(ξ+˜u)⋅E(η+˜v)=ξ⋅η, |
which yields the conclusion.
We denote by
η∈α0(ξ)⇔f0(ξ,η)=ξ⋅η. |
We refer to
Lemma 3.3. Let
v(ω)∈α(u(ω),ω),forμ−a.e.ω∈Ω. | (20) |
Moreover,
E(v)∈α0(E(u)). | (21) |
Proof. Since
f0(ξ,η)=ξ⋅η. | (22) |
Take now
f0(ξ,η)=∫Ωf(ξ+˜u(ω),η+˜v(ω),ω)dμ. | (23) |
Since
ξ⋅η=E(ξ+˜u)⋅E(η+˜v)(12)=∫Ω(ξ+˜u(ω))⋅(η+˜v(ω))dμf∈F(Rn)≤∫Ωf(ξ+˜u(ω),η+˜v(ω),ω)dμ(23)=f0(ξ,η)(22)=ξ⋅η |
from which we obtain
(ξ+˜u(ω))⋅(η+˜v(ω))=f(ξ+˜u(ω),η+˜v(ω),ω),μ-a.e. ω∈Ω. | (24) |
Let
Lemma 3.3 is also referred to as scale disintegration (see [26,Theorem 4.4]), as it shows that given a solution
Lemma 3.4. Let
v(ω)∈α(u(ω),ω),forμ−a.e.ω∈Ω, | (25) |
then
E(v)∈α0(E(u)). | (26) |
Proof. By (25) and (12)
∫Ωf(u(ω),v(ω),ω)dμ=∫Ωu(ω)⋅v(ω)dμ=E(u)⋅E(v). |
On the other hand, by definition of
∫Ωf(u(ω),v(ω),ω)dμ≥f0(E(u),E(v))≥E(u)⋅E(v). |
We conclude that
How the properties of
Theorem 3.5. If
∫Ωf(u(ω),v(ω),ω)dμ<+∞, |
In order to obtain strict monotonicity of
Lemma 3.6. Let
Proof. For all
vi(ω)∈α(ui(ω),ω),for μ-a.e. ω∈Ω | (27) |
and
(η2−η1)⋅(ξ2−ξ1)=∫Ω(v2(ω)−v1(ω))⋅(u2(ω)−u1(ω))dμ≥θ∫Ω|u2(ω)−u1(ω)|2dμ≥θ|∫Ωu2(ω)−u1(ω)dμ|2=θ|ξ2−ξ1|2. |
Let
Lemma 3.7 (Div-Curl lemma, [15]). Let
vn⇀vweaklyinLp′(D;Rm),un⇀uweaklyinLp(D;Rm). |
In addition, assume that
{curlvn} is compact in W−1,p′(D;Rm×m), {div un} is compact in W−1,p(D). |
Then
vn⋅un∗⇀v⋅uin D′(D). |
We are now ready to prove our main result concerning the stochastic homogenization of a maximal monotone relation.
Theorem 3.8. Let
Let
(Jεω,Eεω)∈Lp(D;Rn)×Lp′(D;Rn) |
such that
{divJεω}ε≥0 is compact in W−1,p(D),{curlEεω}ε≥0 is compact in W−1,p′(D;Rn×n), | (28a) |
limε→0Jεω=J0ωweaklyinLp(D;Rn),limε→0Eεω=E0ωweaklyinLp′(D;Rn), | (28b) |
Eεω(x)∈α(Jεω(x),Tx/εω)a.e.inD. | (28c) |
Then, for
E0ω(x)∈α0(J0ω(x))a.e.inD, | (29) |
where
f0(ξ,η):=inf{∫Ωf(ξ+u(ω),η+v(ω),ω)dμ:u∈Vpsol(Ω;Rn),v∈Vp′pot(Ω;Rn)}. |
Proof. By Lemma 3.3 for all
v(ω)∈α(u(ω),ω),for μ-a.e. ω∈Ω. | (30) |
Define the stationary random fields
uε(x,ω):=u(Tx/εω),vε(x,ω):=v(Tx/εω). |
For
x↦uε(x,ω)∈Lploc(Rn;Rn),x↦vε(x,ω)∈Lp′loc(Rn;Rn). |
Equation (30) implies
vε(x,ω)∈α(uε(x,ω),Tx/εω),for a.e. x∈D, μ-a.e. ω∈Ω. | (31) |
By Birkhoff's Theorem (and (13), in particular), for
uε(⋅,ω)⇀E(u)weakly in Lp(D;Rn),vε(⋅,ω)⇀E(v)weakly in Lp′(D;Rn). | (32) |
Since
∫D(Eεω(x)−vε(x,ω))⋅(Jεω(x)−uε(x,ω))ϕ(x)dx≥0, | (33) |
for any
{curl(Eεω−vε(⋅,ω))}ε is compact in W−1,p′(D;Rn×n),{div(Jεω−uε(⋅,ω))}ε is compact in W−1,p(D). |
By (28b), (32), and Lemma 3.7, we can thus pass to the limit as
∫D(E0ω(x)−E(v))⋅(J0ω(x)−E(u))ϕ(x)dx≥0,for μ-a.e. ω∈Ω. |
Since the last inequality holds for all nonnegative
(E0ω(x)−E(v))⋅(J0ω(x)−E(u))≥0,for μ-a.e. ω∈Ω. |
To conclude, since
E0ω(x)∈α0(J0ω(x)) |
for a.e.
Remark 4. In this section's results, the function spaces
U⊂Lp(Ω;Rn),V⊂Lp′(Ω;Rn) |
such that
E(u⋅v)=E(u)⋅E(v),∀(u,v)∈U×V. |
Furthermore, Proposition 1 and Lemma 3.3 remain valid if the previous equality is replaced by the inequality
E(u⋅v)≥E(u)⋅E(v),∀(u,v)∈U×V. |
In this subsection we address the homogenization problem for the Ohm-Hall model for an electric conductor. For further information about the Ohm-Hall effect we refer the reader to [1,pp. 11-15], [12,Section 22] and we also follow [26] for the suitable mathematical formulation in terms of maximal monotone operators. We consider a non-homogeneous electric conductor, that occupies a bounded Lipschitz domain
E(x)∈α(J(x),x)+h(x)J(x)×B(x)+Ea(x)in D, | (34) |
where
curlE=g,divJ=0, |
where the vector field
β(J,x):=α(J,x)+h(x)J×B(x)+Ea(x). |
A single-valued parameter-dependent operator
(β(v1,x)−β(v2,x))⋅(v1−v2)≥θ‖v1−v2‖2∀v1,v2∈R3. | (35) |
The following existence and uniqueness result is a classical consequence of the maximal monotonicity of
Theorem 4.1. Let
|β(x,v)|≤c(1+|v|), | (36) |
β(x,v)⋅v≥a|v|2−b. | (37) |
Let
‖E‖L2+‖J‖L2≤C(1+‖g‖L2) | (38) |
and, denoting by
E(x)=β(J(x),x) inD, | (39) |
curlE(x)=g(x) inD, | (40) |
divJ(x)=0 inD, | (41) |
E(x)×ν(x)=0 on∂D. | (42) |
Moreover, if
Remark 5. Conditions (40)-(41) have to be intended in the weak sense -see below -while (42) holds in
Let
h∈L∞(Ω),B∈L∞(Ω;R3),Ea∈L2(Ω;R3). | (43) |
For any
β(J,ω):=α(J,ω)+h(ω)J×B(ω)+Ea(ω). | (44) |
In order to apply the scale integration procedure, we assume that
the representative function f of β is coercive, in the sense of (14), | (45) |
moreover, to ensure uniqueness of a solution
β and β−1 are strictly monotone, uniformly with respect to x∈D. | (46) |
As in the previous section
βε(⋅,x,ω):=β(⋅,Tx/εω). |
Then
divgε=0,in D′(D), for μ-a.e. ω∈Ω. | (47) |
We are ready to state and prove the homogenization result for the Ohm-Hall model.
Theorem 4.2. Assume that (43)-(47) are fulfilled. Then
1. For
\begin{align} & E_\omega^\varepsilon(x) = \beta_\varepsilon (J_\omega^\varepsilon(x), x, \omega) & &in\;\;\;D, \label{P:incl-eps}\end{align} | (48) |
\begin{align}& {\rm{curl}}\, E_\omega^\varepsilon(x) = g_\varepsilon (x, \omega) & &in\;\;\;D, \label{P:ele-eps}\end{align} | (49) |
\begin{align}& {\rm{div}}\, J_\omega^\varepsilon(x) = 0 & &in\;\;\;D, \label{P:magn-eps}\end{align} | (50) |
\begin{align}&E_\omega^\varepsilon(x) \times \nu(x) = 0 & &on \;\;\;\partial D. \label{P:bound-eps} \end{align} | (51) |
2. There exists
\label{eq:conv} E_\omega^\varepsilon \rightharpoonup E \;\;\;\;\;and\;\;\;\;\; J_\omega^\varepsilon \rightharpoonup J | (52) |
as
3. The limit couple
\begin{align} & E(x) = \beta_0(J(x)) \;\;\;\;\; & &in\;\;\; D, \label{P:incl-hom} \end{align} | (53) |
\begin{align}& {\rm{curl}}\, E(x) = g(x)\;\;\;\;\; & &in\;\;\; D, \label{P:ele-hom} \end{align} | (54) |
\begin{align}& {\rm{div}}\, J(x) = 0 \;\;\;\;\; & &in \;\;\; D, \label{P:magn-hom} \end{align} | (55) |
\begin{align}& E(x) \times \nu(x) = 0 \;\;\;\;\; & &on\;\;\; \partial D. \label{P:bound-hom} \end{align} | (56) |
Proof. 1. Assumption (46) implies that
2. Let
3. The weak formulation of (49)-(51) is:
\label{eq:weak} \int_D E_\omega^\varepsilon \cdot \text{curl}\, \phi + J_\omega^\varepsilon \cdot \nabla \psi\, dx = \int_D g_\varepsilon \cdot \phi\, dx, | (57) |
for all
\int_D E_\omega \cdot \text{curl}\, \phi + J_\omega \cdot \nabla \psi\, dx = \int_D g \cdot \phi\, dx, |
which is exactly the weak formulation of (54)-(56). Equations (49) and (50) imply that
E_\omega(x) = \beta_0(J_\omega(x)). |
We have thus proved that
4. By Lemma 3.6 and assumption (46),
Another straightforward application of the homogenization theorem 3.8 is given in the framework of deformations in continuum mechanics (see, e.g., [4,Chapter 3]). Elastic materials are usually described through the deformation vector
\label{eq:nlelastic} \sigma(x, t) = \beta(\nabla u(x, t), x), | (58) |
where
\rho \partial _{t}^{2}u-\text{div}\sigma =F, |
where
The following existence and uniqueness result is a classical consequence of the maximal monotonicity of
Theorem 4.3. Let
\label{Q:estimates} {\|u\|}_{H^1} +{\|\sigma\|}_{L^2}\leq C\left(1+{\|F\|}_{L^2}\right) | (59) |
and, denoting by
\begin{align} \sigma(x) & = \beta(\nabla u(x), x)\;\;\;\;\; in\;\;\; D, \label{Q:incl}\end{align} | (60) |
\begin{align} -div\, \sigma(x) & = F(x) \;\;\;\;\; in\;\;\; D, \label{Q:ele}\end{align} | (61) |
\begin{align} u(x) & = 0 \;\;\;\;\; on\;\;\; \partial D. \label{Q:bound} \end{align} | (62) |
Moreover, if
As above, we consider a family of maximal monotone operators
\beta_\varepsilon (\cdot, x, \omega): = \beta(\cdot, T_{x/\varepsilon }\omega) |
defines a family of maximal monotone operators on
Theorem 4.4. Assume that (45) and (46) are fulfilled. Then
1. For
\begin{align} & \sigma_\omega^\varepsilon(x) = \beta_\varepsilon (\nabla u_\omega^\varepsilon(x), x, \omega) & &in\;\;\; D, \label{Q:incl-eps}\end{align} | (63) |
\begin{align}& -{\rm{div}}\, \sigma_\omega^\varepsilon(x) = F_\varepsilon (x, \omega) & &in \;\;\;D, \label{Q:ele-eps}\end{align} | (64) |
\begin{align}&u_\omega^\varepsilon(x) = 0 & &on\;\;\; \partial D. \label{Q:bound-eps} \end{align} | (65) |
2. There exist
\label{Q:conv} u_\omega^\varepsilon \rightharpoonup u \;\;\;\;\;and\;\;\;\;\; \sigma_\omega^\varepsilon \rightharpoonup \sigma | (66) |
as
3. The limit couple
\begin{align} & \sigma(x) = \beta_0(\nabla u(x)) & &in\;\;\; D, \label{Q:incl-hom}\end{align} | (67) |
\begin{align}& -{\rm{div}}\, \sigma(x) = F(x) & &in \;\;D, \label{Q:ele-hom}\end{align} | (68) |
\begin{align}& u(x) = 0 & &on\;\;\; \partial D. \label{Q:bound-hom} \end{align} | (69) |
Proof. Steps 1. and 2. follow exactly as in the proof of Theorem 4.2.
3. The weak formulation of (64)-(65) is the following:
\label{Q:weak} \int_D \sigma_\omega^\varepsilon \cdot \nabla \phi\, dx = \int_D F_\varepsilon \phi\, dx, | (70) |
for all
\int_D \sigma_\omega \cdot \nabla \phi\, dx = \int_D F \phi\, dx, |
which is exactly the weak formulation of (68)-(69). Equation (64) and estimate (59) imply that
{{\{\text{div}\sigma _{\omega }^{\varepsilon }\}}_{\varepsilon \ge 0}}\text{ is compact in }{{W}^{-1,2}}(D;{{\mathbb{R}}^{3}}), |
{{\{\text{curl}\nabla u_{\omega }^{\varepsilon }\}}_{\varepsilon \ge 0}}\text{ is compact in }{{W}^{-1,2}}(D;{{\mathbb{R}}^{3\times 3}}). |
Therefore, we can apply the abstract stochastic homogenization Theorem 3.8, (with
\sigma_\omega(x) = \beta_0(\nabla u_\omega(x)). |
Finally, the strict monotonicity of the limit operators
We would like to thank the anonymous referees for their valuable comments and remarks.
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