Abstract
In this paper, we investigate the Cauchy problem for the coupled generalized Korteweg–de Vries system driven by a cylindrical Wiener process. We prove local well-posedness for data in with . The key methods that we used in this paper are multilinear estimates in Bourgain spaces, the Itô formula, and a fixed-point argument.
Keywords:
stochastic partial differential equations; white Noise; coupled KdV system; Bourgain space MSC:
60H15; 49K40; 60H40
1. Introduction
Dispersive nonlinear systems appear in many physical applications. They can be used, for example, to model wave propagation on the surface of water or to describe the interaction of nonlinear internal waves. In this study, we focus on Hamiltonian systems.
where and are real valued functions, N is a smooth function, and denote the derivative of N with respect to and , respectively, and is real constant which we normalize to .
This system generalizes the classical Korteweg–de Vries equation [1], originally introduced to model shallow water waves.
The question of the correct local and global definition of the initial value problem associated with (1) has been a major topic in the theory of dispersive equations in the last few years. We shall briefly recall some results of interest to us that are available in the current literature.
For , the system (1) is a particular case of the Majda–Biello system [2], which models the nonlinear interaction of long-wavelength equatorial Rossby waves and barotropic Rossby waves. The well-posedness problem associated with initial value problem (1) in this case has been studied by many authors. Tadahiro Oh [3] proved that the initial value problem is locally and globally well-posed in both cases:
- In the periodic case, it is locally well-posed for , where , and globally well-posed in due to the conservation of the Hamiltonian.
- In the nonperiodic case, it is locally well-posed for , where , and globally well-posed in due to the conservation law.
For , system (1) reduces to a special case of a broad class of nonlinear evolution equations considered by Ablowiz et al. [4] in the context of inverse scattering. In this case, questions regarding the proper positioning as well as the existence and stability of solitary waves for this system have been extensively studied in the literature. Carvajal and Panthee [5] proved that the initial value problem (1) is locally well-posed for given data , where .
For and given by
Gomes and Pastor [6] proved that the system is locally well-posed for given data , where , and globally well-posed in .
In [7], Grujić and Kalisch establish local well-posedness for the initial-value problem
for initial data in , where for , and for . The solution persists in for some , remaining analytic on the same fixed strip of width throughout the time interval .
In [8], de Bouard and Debussche studied the stochastic KdV equation with white noise forcing. They proved existence and uniqueness of solutions in for additive noise, and existence of martingale solutions in for multiplicative noise (e.g., [9,10,11,12]).
Boukarou et al. [13] proved that the white-noise-driven KdV-type Boussinesq system
is locally well-posed for given data , where , and globally well-posed in (e.g., [14,15,16]).
We are interested in stochastic system with the Hamiltonian structure, which is a coupled system of generalized Korteweg–de Vries equations given by (2) and (3).
with
where is a natural number, and and D non-negative real constants. Here, and are random processes defined for , is a linear operator, and is a cylindrical Wiener process on , which can also be given by , where is an orthonormal basis of , and is a sequence of mutually independent real Brownian motions in a fixed probability space.
In this work, we improve the local well-posedness result of Gomes and Pastor [6] by extending their deterministic analysis to the stochastic setting and lowering the required Sobolev regularity. Building on the methods introduced in [7,8,11], we prove that the stochastic system of (2) and (3) is locally well-posed for initial data in with .
The regularity threshold is critical for KdV-type systems, as it reflects the intrinsic balance between dispersive effects and nonlinear interactions within the Bourgain space framework. Improving the previously known requirement of to constitutes a meaningful advancement in the low-regularity theory for coupled stochastic dispersive equations.
To state our results precisely, let us first introduce the notation to be used. We begin with function spaces. For denotes the usual Sobolev space of order s, defined by the norm
where is the spatial Fourier transform
Similarly, for the Bourgain spaces and are defined by the norms
where denotes the spacetime Fourier transform
For , we also use the spaces and of restrictions to the time interval of functions in and . They are endowed with the norms
Since we are dealing with system of equations, we will need to consider product function spaces. For this, we define the product spaces
with norms
and
The mixed -norm is defined by
Finally, we denote as the space of Hilbert–Schmidt operators from into with the norm
where is an orthonormal basis in .
With this notation in place, we may finally state the results to be proven. In the subsequent work, let be a fixed probability space adapted to a filtration . For system (2), we will prove the following local results.
Theorem 1.
Assume that and , and b is close enough to . If for almost surely and are measurable, then for almost surely , there exists a constant and a unique solution u of the Cauchy problem (2) on which satisfies
Remark 1.
The solution satisfies , which follows from the embedding for .
Notation: Throughout this paper, we use the following standard asymptotic notation:
- denotes with a constant depending on . If c is an absolute constant, we shall write .
- means that a and b are asymptotically equivalent.
- means that a and b are comparable in size, typically with implicit constants independent of the parameters.
- means that a is much smaller than b, typically in the sense that the ratio is bounded by a small constant.
This paper is organized as follows: Section 2 introduces the notation and linear estimates. Section 3 establishes multilinear estimates in Bourgain spaces. Section 4 provides stochastic convolution estimates. Section 5 proves local well-posedness via a fixed-point argument. Section 6 concludes the study and presents remarks and future directions.
2. Linear Estimates
To prove our main results, we will need to introduce several important estimates. To state these estimates, let us write the Itô form of the system in Equation (2), namely
System (4) is supplemented with the initial conditions
To understand the assumptions required for , it is useful to first consider the linear equation
which is given by the stochastic Itô integral
where comprise the Airy group. Using the unitarity of and , one can easily show that and belong to only if is a Hilbert–Schmidt operator from into .
We will solve (4) supplemented with the initial condition (5) by considering its mild form
To construct mild solutions, we will need the following estimates:
Proposition 1
(Linear Estimates [5]). For any , we have
Further, if and , then
and
3. Multilinear Estimates
The proof of multilinear estimates will be established using a number of auxiliary results. To state these results, we first need to introduce some more notation. We note that the operators and are defined as
Lemma 1.
([7]). Let s and κ be given. There is a constant c depending on s and κ such that
Lemma 2.
For , , and , there exists a constant C depending only on κ and s, such that
Proof.
Since and , we can choose b such that and . For example, set b sufficiently close to that , which is possible because . Then is bounded in this region.
We begin by applying the Sobolev embedding theorem in the time variable. For , we have the continuous embedding , which gives
where is the Fourier multiplier operator in time defined by
Now observe that
According to Plancherel’s theorem in , we compute
Thus, it suffices to show that the multiplier
is bounded on . We consider two cases:
- Case 1: .
- In this case, we have , and also
- Therefore,
Case 2: .
In this case, we have for large , so that
Hence,
Since and , this is bounded as and . Therefore, , and we conclude that
□
Lemma 3.
For , , , and , there exists a constant C depending only on κ, s, and α, such that
Proof.
For , we have , which gives
where is the Fourier multiplier operator in time. Then
According to Plancherel’s theorem in , we compute
Thus, it suffices to show that the multiplier
is bounded on . We consider two cases:
Case 1: .
In this case, we have , and also
Therefore,
Since and , we can choose b such that and . Then is bounded in this region.
Case 2: .
In this case, we have for large , so that
Hence,
Since and , this is bounded as and . Therefore, , and we conclude that
□
Lemma 4
(Multilinear estimate). Let , ; then there exist and such that for all and , we have
hold for any .
Proof.
First of all, for and , we define
We first prove the case , as the proof for general will then be more transparent, which means we prove
Inequality (14): We have
where .
Using duality, we prove this estimate, where is a positive function in with norm ; then
Now, we split the Fourier space into six regions as follows:
We begin with region 1:
so
We assume that or .
Firstly, for case ,
According to the last inequality and , we obtain
By calculating
we get
We suppose that
Then
By using Cauchy–Schwarz’s inequality for the variables x and t, we get
Hence, according to Lemmas 1 and 2,
Secondly, for the case , we have
Then
Then, by calculating the inner product, we have
Hence, according to Lemmas 1 and 2,
The inequalities in the other five regions are proven in a similar manner.
The case is handled virtually identically. The only difference is that we need to split the Fourier space in . We want to prove that
We have
In the same way, by calculating the inner product, we have
Inequality (15): We have
We begin with region 1: We assume that or .
Firstly, for case ,
We suppose that
Then
By using Cauchy–Schwarz’s inequality for the variables x and t, we get
Hence, according to Lemmas 1 and 3,
Secondly, for the case , similarly, here we use
For the case , we have
In the same way, by calculating the inner product, we have
The case is calculated similarly though induction; see, e.g., [17] for analogous multilinear estimates in deterministic generalized Korteweg–de Vries systems. □
4. Stochastic Estimates
We introduce a smooth temporal cutoff function satisfying
This function localizes the stochastic convolution to the time interval , which allows us to work within Bourgain spaces on . Note that for any , such a belongs to the fractional Sobolev space , whose norm is given by
We show the following lemma.
Lemma 5
Proof.
Let us introduce the function
This implies that . Thus, we have
From the Itô isometry formula, we have
To estimate , we decompose the double integral over into three distinct regions, corresponding to the cases where and lie in different intervals relative to the support of the cutoff function .
We now bound , and separately:
Using Equation (23) and the assumption that , we have
Similarly,
So, we have
where . □
5. Local Well-Posedness
Using the stochastic estimates from the previous section and the Banach fixed-point theorem, we now establish a local well-posedness result for system (2). This section is therefore dedicated to the proof of Theorem 1. Let
and
Then, we may rewrite (7) in terms of
as
with
Then (24) and (25) are equivalent to
Next, we define the ball as
The goal of this section is thus to prove that the mapping is a contraction on . According to Lemmas 1, 4, and 5, we obtain
so
Therefore, for , we get
so
First, we define
Then, we choose a small enough such that
One can easily verify that maps into itself and is a strict contraction on with respect to the norm ,
Hence, has a unique fixed point in , which is a solution to (26) on .
Now observe that
We complete the proof by showing that . Recall that and . According to the Sobolev embedding theorem, we have .
We need the following theorem:
Theorem 2
([9]). Assume that generates a contraction semigroup and . Then the process has a continuous modification and there exists a constant C such that
Under the condition that , and the fact that and are a unitary group in , an application of Theorem 2 implies that .
According to Lemma 4, we have and for any extension of in , and of in , where , and applying Lemma 3.2 in [18], we get
Since ,
6. Conclusions
In this work, we have established local well-posedness for a system of coupled generalized KdV equations driven by cylindrical noise in the Sobolev space for . The proof relies on a combination of Bourgain space techniques, multilinear estimates, and stochastic analysis, thereby extending and improving previous deterministic well-posedness results by both lowering the required regularity threshold and incorporating stochastic forcing.
A natural extension of this work is the study of the corresponding initial-boundary value problem on the half-line, for which the Fokas unified transform method provides a powerful analytical framework [19]. In this setting, the boundary behavior of solutions to stochastic dispersive systems remains an open and challenging problem, with recent progress in the linear case offering a useful starting point [20]. Further research directions include investigating global well-posedness, blow-up phenomena, and the effect of more singular noise or higher-dimensional settings.
Author Contributions
A.B.: Conceptualization, Methodology, Formal Analysis, Writing—Original Draft Preparation, Supervision; M.B.J.: Investigation, Methodology; N.A.A.: Investigation, Methodology. All authors have read and agreed to the published version of the manuscript.
Funding
This work is supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2502).
Data Availability Statement
The data are contained within the article.
Conflicts of Interest
The authors declare no conflicts of interest.
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