We studied stochastic homogenization of a quasi-linear parabolic partial differential equation (PDE) with nonlinear microscopic Robin conditions on a perforated domain. The focus of our work lies in the underlying geometry that does not allow standard stochastic homogenization techniques to be applied directly. Instead, we introduced a concept of regularized homogenization: We proved homogenization on a regularized but still random geometry and demonstrated afterwards that the form of the homogenized equation was independent from the regularization, though the explicit values of the coefficients depended on the regularization. Then, we passed to the regularization limit to obtain the anticipated limit equation where the coefficients were finally independent from the intermediate regularizations. We provided evidence that the regularized homogenization and the classical stochastic homogenization coincided on geometries that indeed allowed stochastic homogenization. Furthermore, we showed that Boolean models of Poisson point processes were covered by our approach.
Citation: Martin Heida, Benedikt Jahnel, Anh Duc Vu. Regularized homogenization on irregularly perforated domains[J]. Networks and Heterogeneous Media, 2025, 20(1): 165-212. doi: 10.3934/nhm.2025010
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We studied stochastic homogenization of a quasi-linear parabolic partial differential equation (PDE) with nonlinear microscopic Robin conditions on a perforated domain. The focus of our work lies in the underlying geometry that does not allow standard stochastic homogenization techniques to be applied directly. Instead, we introduced a concept of regularized homogenization: We proved homogenization on a regularized but still random geometry and demonstrated afterwards that the form of the homogenized equation was independent from the regularization, though the explicit values of the coefficients depended on the regularization. Then, we passed to the regularization limit to obtain the anticipated limit equation where the coefficients were finally independent from the intermediate regularizations. We provided evidence that the regularized homogenization and the classical stochastic homogenization coincided on geometries that indeed allowed stochastic homogenization. Furthermore, we showed that Boolean models of Poisson point processes were covered by our approach.
The theoretical study of weakly coupled limit cycle oscillators is being actively developed in several research fields. For example, in statistical physics, various models are being developed, whereas in network science, synchronization on complex networks is attracting attention. As we state later, mathematical analysis of this field is being promoted, especially by those who are concerned with functional equations. Furthermore, this phenomenon is applied to various areas of engineering, including neural networks, bio-sciences, and network engineering[9].
Limit cycle oscillators, which are also called nonlinear oscillators, are different from harmonic oscillators, whose limit cycle is vulnerable to the perturbation forces from outside. The synchronization phenomenon is another feature of coupled limit cycle oscillators under specific conditions.
As is well known, such a phenomenon was first discovered by Huygens in the 17th century, who devised the pendulum clock for navigation officers. The physical formulation of this phenomenon was rigorously discussed later in the 1960's. In 1967, Winfree[37] proposed an attractive formulation, in which he successfully and rigorously defined the phase of the oscillator. He also revealed the region of synchronization in the sense of the phase space.
Based on Winfree's contribution, Kuramoto[19] proposed a simplified but more sophisticated model, which is called the Kuramoto model. The features of his approach are phase reduction and mean field approximation. His discussion begins with the usual dynamical system:
dXdt=F(X), |
where X is the
dϕdt=ω, |
where
dϕdt=∇ϕ⋅F(X), |
which is the start point of phase reduction. In an
dϕjdt=ωj+N∑k=1Kjksin(ϕj−ϕk)(j=1,2,…N), | (1.1) |
where
Kuramoto[17][19] further sophisticated (1.1) by applying the mean field approximation and the assumption
{dϕjdt=ωj+Krsin(η−ϕj)(j=1,2,…N),rexp(iη)=1NN∑j=1exp(iϕj), | (1.2) |
where
Since this model is sufficiently simple for rigorously analyzing and simulating on computers, numerous investigations have been conducted on it. For instance, Daido[8] derived a model that replaces the term
As for the stability analysis, the work by Strogatz and Mirollo[34] is the first one, which concerns the spectrum of the incoherent state as the number of oscillators tends to infinity. Later, Crawford[6][7] applied the center manifold reduction to verify the stability of incoherence in detail.
Due to the limitation of space, we refer the reader to the survey by Acebrón[3] of this research area.
From the perspective of network science, it is interesting to generalize the network topology and distribution of coupling strength. Ichinomiya[14] proposed a model on random networks, and Nakao[29] numerically analyzed a model on a complex network.
Although (1.2) is a system of ordinary differential equations, adding white noise to it makes it possible to apply partial differential equation-based analysis. We consider
{dϕjdt=ωj+Krsin(η−ϕj)+ξj(t) ≡V1(ϕj,t,ωj)+ξj(t)(j=1,2,…N),rexp(iη)=1NN∑j=1exp(iϕj), | (1.3) |
where
<ξj(t)>=0, <ξj(t)ξk(τ)>=2Dδ(t−τ)δjk, |
{∂ϱ∂t+∂∂θ[(ω+Krsin(η−θ))ϱ]−D∂2ϱ∂θ2=0,rexp(iη)=∫R∫2π0exp(iθ)ϱ(θ,t;ω′)g(ω′)dϕdω′, | (1.4) |
where
Remark 1. Let
Kj(x)=limτ→0⟨(X(τ)−X(0))j⟩/τ. |
Then, the probability distribution function
∂w(x,t)∂t=∞∑j=11j!(−∂∂x)j{Kj(x)w(x,t)}. | (1.5) |
A Markovian process satisfying
Kj(x)=0 (j=3,4,…) |
is called to be continuous. One example of this type of stochastic process is the one driven by the Brownian motion, like (1.3). In this case, (1.5) becomes
∂w(x,t)∂t=−∂∂x[K1(x)w(x,t)]+12∂2∂x2[K2(x)w(x,t)], |
which is the Fokker-Planck equation. Applying this argument to (1.3), where
∂ϱ∂t+∂∂θ(V1ϱ)−D∂2ϱ∂θ2=0, |
which corresponds to (1.4)
By substituting (1.4)
{∂ϱ∂t+ω∂ϱ∂θ+K∂∂θ[ϱ(θ,t;ω)∫R∫2π0sin(ϕ−θ)ϱ(ϕ,t;ω′)g(ω′)dϕdω′] −D∂2ϱ∂θ2=0θ∈(0,2π),t>0,ω∈R,∂iϱ∂θ|θ=0=∂iϱ∂θ|θ=2π(i=1,2),t>0,ω∈R,ϱ|t=0=ϱ0θ∈(0,2π),ω∈R, | (1.6) |
which is the so-called Kuramoto-Sakaguchi equation[24]. This approach enables macroscopic analysis when a system consists of numerous oscillators. As we state later, (1.6) with
Kuramoto also presented a direction to take the spatial validity of coupling strength into account in his original model, which he called the non-local coupling model. In it, the strength of the connection between oscillators depends on the distance between them[21]. Due to the varying strength of connections, it was shown that characteristic patterns, such as a chimera pattern, emerge[21][22]. Numerical studies of the chimera state have also been conducted, mainly by Abrams and Strogatz[1][2].
In spite of numerous contributions concerning numerical simulations, there have been few studies regarding the mathematical analysis of this model. In this paper, we discuss the existence and uniqueness of the global-in-time solution to this model. We also discuss the nonlinear stability of the trivial stationary solution and existence of the vanishing diffusion limit. We note that the vanishing diffusion limit is discussed in the function spaces of higher order derivative than that by Ha and Xiao's[11] by applying a different approach.
The remainder of this paper is organized as follows. In the next section, we formulate the problem of the phase reduction of non-local coupling oscillators, including both with and without diffusion. In Section 3, we introduce related work. In Sections 4 and 5, we are concerned with the local and global-in-time solvability of the problem and the stability of the stationary incoherent solution, respectively. We discuss the existence of the solution in the vanishing diffusion limit in Section 6, and provide a conclusion and discuss remaining issues in Section 7.
In this section, we formulate the problem to be considered.We begin the discussion with the temporal evolution of the phase with additive noise under non-local coupling [15][20][31]:
dϕdt(x,t)=ω+∫RG(x−y)Γ(ϕ(x,t)−ϕ(y,t))dy+ξ(x,t), | (2.1) |
where
<ξ(x,t)ξ(y,τ)>=2Dδ(x−y)δ(t−τ) |
with
It is also to be noted that
∫RG(x−y)dy∫Rg(ω′)dω′∫2π0Γ(ϕ−ϕ′)ϱ(ϕ′,t;y,ω′)dϕ′, |
where
dϕdt=ω+∫RG(x−y)dy∫Rg(ω′)dω′∫2π0Γ(ϕ−ϕ′)ϱ(ϕ′,t;y,ω)dϕ′+ξ(x,t) ≡V2(ϕ,t,x,ω)+ξ(x,t). | (2.2) |
If we consider the case that
K∫Rg(ω′)dω′∫2π0Γ(ϕ−ϕ′)ϱ(ϕ′,t;ω′)dϕ′, |
and (2.2) becomes
dϕdt=ω+K∫Rg(ω′)dω′∫2π0Γ(ϕ−ϕ′)ϱ(ϕ′,t;ω)dϕ′+ξ(x,t), |
which corresponds to (1.3) as a infinite population limit of it.
Note that (2.2) is the mean field approximation to (2.1), in the sense that we replace the second term of the right-hand side of (2.1) with its average. However, while (1.4)
By tracing the same argument as we derived (1.4), the Fokker-Planck equation corresponding to (2.2) is written as (hereafter we denote the phase by
∂ϱ∂t+∂∂θ(V2ϱ)−D∂2ϱ∂θ2=0. |
Together with suitable initial and boundary conditions, the problem corresponding to (2.2) is written as[15][31].
{∂ϱ∂t+∂∂θ(ωϱ)+∂∂θ(F[ϱ,ϱ])−D∂2ϱ∂θ2=0 θ∈(0,2π),t>0,(x,ω)∈R2,∂iϱ∂θi|θ=0=∂iϱ∂θi|θ=2π(i=0,1),t>0,(x,ω)∈R2,ϱ|t=0=ϱ0 θ∈(0,2π),(x,ω)∈R2, | (2.3) |
where
F[ϱ1,ϱ2]≡ϱ1(θ,t;x,ω)∫RG(x−y)dy∫Rg(ω′)dω′∫2π0Γ(θ−ϕ)ϱ2(ϕ,t;y,ω′)dϕ. | (2.4) |
It is to be noted that, in[15], [21] and[31], they deal with problem (2.3) with
˜F[ϱ1,ϱ2]≡ϱ1(θ,t;x)∫RG(x−y)dy∫2π0Γ(θ−ϕ)ϱ2(ϕ,t;y)dϕ, |
which corresponds to the case
In (2.3), the unknown function is
F(k)[ϱ1,ϱ2]≡ ϱ1(θ,t;x,ω)∫RG(x−y)dy∫Rg(ω′)dω′∫2π0Γ(k)(θ−ϕ)ϱ2(ϕ,t;y,ω′)dϕ (k=1,2,…). |
Note that we denote the
As in the original Kuramoto model, the vanishing diffusion case is worth considering:
{∂ϱ∂t+∂∂θ(ωϱ)+∂∂θ(F[ϱ,ϱ])=0θ∈(0,2π),t>0,(x,ω)∈R2,∂iϱ∂θi|θ=0=∂iϱ∂θi|θ=2π (i=0,1),t>0,(x,ω)∈R2,ϱ|t=0=ϱ0 θ∈(0,2π),(x,ω)∈R2. | (2.5) |
It is obvious that
ϱ(θ,t;x,ω)≥0 ∀θ∈(0,2π),t∈(0,T),(x,ω)∈R2,∫2π0ϱ(θ,t;x,ω)dθ=1 ∀t∈(0,T),(x,ω)∈R2 |
for arbitrary
Mathematical arguments concerning the solvability of the Kuramoto-Sakaguchi equation (1.6), which corresponds to the original Kuramoto model (1.3), was first presented by Lavrentiev et al.[24][25]. In their former work[24], they constructed the classical global-in-time solution when the support of
Later, they removed this restriction[25] by applying the a-priori estimates derived from the energy method. They also studied the regularity of the unknown function with respect to
For the case of vanishing diffusion
Concerning the non-local coupling model, however, there are no mathematical arguments as far as we know.
In this section, we discuss the global-in-time solvability of (2.3). We first prepare the definitions of function spaces.
In this subsection, we define the function spaces used throughout this paper. Let
‖ |
The inner product is defined by
where
For simplicity, we denote the
Hereafter, let us use the notation
By
For a Banach space
Likewise, we denote by
Subject to the definition by Temam[35], we say, for a fixed parameter
which is expanded in the Fourier series, belongs to the Sobolev space
Due to the definition of the Fourier series, the Fourier coefficients
Note that in case
We also introduce the notation
where
First, we state the existence and uniqueness of the local-in-time solution to problem (2.3).
Theorem 4.1. Let us assume
(ⅰ)
(ⅱ)
(ⅲ)
(ⅳ)
(ⅴ)
(ⅵ)
Then, there exists a certain
where
Before proceeding to the proof of Theorem 4.1, we prepare the following lemmas.
Lemma 4.2. Let
(ⅰ) If
(ⅱ)
(ⅲ)
Proof. Here we only show the proof of (ⅱ). By virtue of the Schwarz inequality, we have
The estimate (ⅲ) is obtained in a similar manner, and statement (ⅰ) is obtained easily.
Hereafter, for arbitrary
Lemma 4.3. For an arbitrary
(ⅰ)
(ⅱ)
Remark 2. If
Proof. We verify the statement by using the Stampacchia's truncation method. Let us define
It is obvious that
Here we used the estimate:
which is derived by integration by parts and Lemma 4.2. Taking into account
We carry out the proof of Theorem 4.1 in three steps below.
(ⅰ) Existence of a solution
(ⅱ) proof of
(ⅲ) uniqueness of the solution in
We apply the semi-discrete approximation used by Sjöberg[32] and Tsutsumi[36] for the study of the KdV equation. Let us take
Now, instead of problem (2.3), we consider the following differential-difference equation:
(4.1) |
where
Note that this function is defined on the continuous interval with respect to
By virtue of the estimate (4.9) we will verify later, it is clear that problem (4.1) above has a unique solution
respectively. As Sjöberg[32] and Tsutsumi[36] did, we assume
forms an orthonormal basis with respect to the scalar products
The following lemmas are due to the work by Sjöberg[32] and Tsutsumi[36]; thus, we omit the proof here.
Lemma 4.4. If
(4.2) |
(4.3) |
Lemma 4.5. Let
Then,
holds with some constants
For the proof of Lemma 4.5, see Lemma 2.2 in the work by Sjöberg[32]. In addition, the following lemma is useful.
Lemma 4.6. Let
with
holds for non-negative integers
Proof. We first verify the statement when
noting that
(4.4) |
where
On the other hand, since
we have
Accordingly, by noting (4.4) again, we have
Finally, the statement holds in case
(4.5) |
On the other hand,
However, it is easy to see that this equals the rightmost-hand side of (4.5); therefore,
On the basis of Lemma 4.6, we derive some estimates of
Lemma 4.7. The following estimates hold:
where
Proof. Let us multiply (4.1)
(4.6) |
Making use of
for two real
where
and by means of the mean value theorem and Schwarz's inequality,
(4.7) |
where
where we take
By virtue of the comparison theorem,
which has a solution on
(4.8) |
with a constant
Combining these, we have
By taking
which yields
for
Similarly, multiplying (4.1)
We expand the last term in the left-hand side as
Each term is estimated as follows.
Here we applied a similar estimate as (4.7). Thus, by using (4.8) we have the estimate of the form
which yields the boundedness of
When
(4.9) |
under the assumptions of Theorem 4.1. Then, as Sjöberg[32] and Tsutsumi[36] did, we consider the discrete Fourier series of
Estimate (4.9) and Lemmas 4.5-4.7 yield that the sequence of functions
in
Finally, we discuss the regularity of
which clearly satisfy
Thus, under the assumptions of Theorem 4.1, we can show that
with respect to
Before proceeding to the uniqueness part, we mention that the solution that was guaranteed to exist in the previous process also belongs to
Finally, we discuss the uniqueness part of the statement. Assume that there exist two solutions
Then, it satisfies:
(4.10) |
We multiply (4.10)
These yield
By virtue of the Gronwall's inequality and the initial condition (4.10)
which indicates the uniqueness of the solution in the desired function space.
Now we discuss the global-in-time solvability of (2.3). Let
Lemma 4.8. Let
(4.11) |
hold with certain constants
Proof. For the sake of simplicity, we introduce the notation
(4.12) |
Multiply (4.12)
On the other hand, in the same line as the arguments by Lavrentiev[24], we have
where we have applied the Young's inequality in the last inequality.
Thus, after taking the supremum with respect to
(4.13) |
Therefore, if we take
(4.14) |
Next, we show the estimate of
Then, due to the estimates
and the Young's inequality, and taking the supremum with respect to
(4.15) |
with constants
to the first term:
Then, we obtain
Summing up this and (4.15) multiplied by a positive constant
Therefore, we take
(ⅰ) Take
(ⅱ) Then, take
hold.
Then, in the same line with the deduction of (4.14), we have
(4.16) |
Similarly, for
(4.17) |
For estimates of
with a constant
for functions
Now, by operating
(4.18) |
where
Operating
(4.19) |
Hereafter we use the notation
Then, we multiply (4.19) by
(4.20) |
with constants
(4.21) |
We now multiply each estimate for
It should be noted that a straightforward estimate, like that by Ha and Xiao[11] will require
(4.22) |
We take
(ⅰ) Take
(ⅱ) Take
hold;
(ⅲ) Take
hold;
(ⅳ) As for
hold;
(ⅴ) Finally, take
hold.
Thus, (4.22) becomes
where the coefficients of each term in the left-hand side are all positive. Thus, as we have obtained (4.14) and (4.16), the Gronwall's inequality again yields
Now, by virtue of Lemma 4.8,
Theorem 4.9. Let
Remark 3. From these considerations, it is obvious that Theorem 4.9 holds when
Corollary 4.10. Under the assumptions in Theorem 4.1 with (ⅳ) replaced by
In this section, we discuss the nonlinear stability of the trivial stationary solution
The asymptotic stability of
Theorem 5.1. In addition to the assumptions in Theorem 4.1, we assume
Then,
with certain positive constants
Proof. The line of the argument is similar to that of Lemma 4.8, but this time we have to confirm the non-positiveness of the left-hand side of the energy type inequalities. First, let us multiply (4.12)
Thus, we have the energy estimate
(5.1) |
For the estimate of
which leads to the estimate of the form
(5.2) |
by virtue of the Gronwall's inequality. Estimate (5.2) implies the asymptotic stability of
Next, we show the estimate up to the first-order spatial derivative. First, by applying the Poincaré's inequality to the second term of the left-hand side in (5.1) partially, as in the previous section, we have
(5.3) |
Then, let us sum up (5.3) and (4.15), and we have
(5.4) |
As in the previous section, we take
(ⅰ) Take
(ⅱ) Then, take
hold.
By applying the Gronwall's inequality to (5.4), these lead to the estimate of the form
that is, the asymptotic stability of
Similarly, we make use of (4.17) and (4.21) to deduce
(5.5) |
(5.6) |
Summing up (5.3), (5.5) and (5.6) multiplied by constants
Now, we determine
(ⅰ) Take
(ⅱ) Take
hold;
(ⅲ) Take
hold;
(ⅵ) As for
hold;
(ⅴ) Finally, take
hold.
These yield the estimate of the form
which directly leads to the desired statement.
Remark 4. For the original Kuramoto-Sakaguchi equation (1.6), Ha[11] deduced a similar result concerning the asymptotic stability of
Finally, we discuss the vanishing limit of the diffusion coefficient. In order to show the dependency of the solution on the diffusion coefficient clearly, we denote the solution of (2.3) as
Lemma 6.1. Let
Proof. What we have to verify are
(6.1) |
(6.2) |
with some constants
Now we provide the estimates of the temporal derivative of
and Lemma 4.2, we have the estimate of the form
(6.3) |
where
(6.4) |
As for
(6.5) |
where
We operate the temporal derivative
(6.6) |
Then, after the energy type estimate, we make
we obtain (6.4) with
Next, we estimate the term including both temporal and spatial derivatives. We only show the case
(6.7) |
Now we show some examples of the energy type estimates. In case
For
Otherwise, we have
By combining these and (6.7), and applying the Young's and Schwarz's inequalities, we derive the energy estimate of the form
(6.8) |
After making
Thus, we have shown the first part of the statement. Estimate (6.2) is derived from (6.3), (6.8), and the estimates we have already obtained. These complete the proof.
By virtue of Lemma 6.1, we see that the sequence
(6.9) |
(6.10) |
Then, in the relationship
if we make
which means
The next lemma clarifies the space to which this sequence converges.
Lemma 6.2. Let
Proof. Let us define
Then, with the aid of the estimates
we have
Thus, by virtue of the Gronwall's inequality and the fact that
This implies that the sequence
where
We show inductively that
In case
Otherwise, we have
After applying the Schwarz's inequality, we make
where
From these considerations, the sequence
By Lemma 6.2, we see that
(6.11) |
In virtue of (6.9)-(6.10), if we make
On the other hand, thanks to the Rellich's theorem[27], we have
strongly as
holds. Thus, we arrive at
(6.12) |
which means that
(6.13) |
(6.14) |
respectively. Comparing (6.13) and (6.14) with the aid of (6.9)-(6.10) implies
Theorem 6.3. Let
In this paper, we discussed the mathematical analysis of the nonlinear Fokker-Planck equation of Kuramoto's non-local coupling model of oscillators. We first showed the local and global-in-time solvability, and then the nonlinear asymptotic stability of the incoherent state. Finally, we verified the existence of the vanishing diffusion limit solution as the diffusion coefficient tends to zero.
Our future work will be concerned with the mathematical stability analysis of the chimera state of this model and the coupled oscillator model on the complex graph. We will also tackle the bifurcation problem.
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