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Global well-posedness of strong solution to the kinetic Cucker-Smale model with external potential field

  • In this paper, we studied the initial problem of the kinetic Cucker-Smale model with noise, driven by pairwise alignment interactions under the influence of external potential field. Without the general compact support or smallness assumption on the initial datum, we established the global existence of strong solution. The proof was based on weighted energy estimates.

    Citation: Linglong Du, Anqi Du, Zhengyan Luo. Global well-posedness of strong solution to the kinetic Cucker-Smale model with external potential field[J]. Networks and Heterogeneous Media, 2025, 20(2): 460-481. doi: 10.3934/nhm.2025021

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  • In this paper, we studied the initial problem of the kinetic Cucker-Smale model with noise, driven by pairwise alignment interactions under the influence of external potential field. Without the general compact support or smallness assumption on the initial datum, we established the global existence of strong solution. The proof was based on weighted energy estimates.



    This paper is concerned with the following kinetic Cucker-Smale model with external potential field:

    {tf+vxfxU(x)vf+v(L[f]f)=σΔvf,f(t,x,v)|t=0=f0(x,v). (1.1)

    Here, f=f(t,x,v) is the particle distribution function in space (x,v)Ω=Rd×Rd, at time t0. The function U(x)=12|x|2 represents the harmonic potential field. The constant σ>0 represents the noise strength. The alignment force L[f] is expressed in the form:

    L[f](t,x,v)=Ωφ(|xy|)f(t,y,v)(vv)dydv.

    The interaction kernel function φ(|xy|) is a positive nonincreasing C2 function. Without loss of generality, we assume that

    max{|φ|,|φ|,|φ|}1.

    The system (1.1) arises as a mean-field kinetic description of the following stochastic Cucker-Smale model with external potential field:

    {dxi=vidt,dvi=1NNj=1φ(|xixj|)(vjvi)dtxU(xi)dt+σdW(i)t,i=1,,N, (1.2)

    where the deterministic system was studied by [1]. Here, (xi(t),vi(t)) are the position and velocity pair of ith-particle, W(i)t denote independent Wiener processes, and σ is the magnitude of the noise. The communication weight function φ:RdR+ satisfies some symmetry conditions.

    The particle Cucker-Smale model was originally proposed to understand the flocking phenomena in bird populations by Cucker and Smale [2,3]. Under the "molecular chaos" assumption, Ha and Tadmor [4] derived the kinetic Cucker-Smale model formally from the particle Cucker-Smale model using the BBGKY hierarchy method, e.g., [5,6,7,8]. For large-scale particle systems, Ha and Liu [9] rigorously justified the mean-field limit from the multi-particle Cucker-Smale model to the kinetic Cucker-Smale model, utilizing tools such as measure-valued solutions and the Kantorovich-Rubinstein distance. Furthermore, Carrillo et al. [10] proved that the solutions approached exponentially fast in velocity to the mean velocity of the initial condition, while in space they converged to a translational flocking solution.

    The Cucker-Smale model has been extended to various complexities, including the presence of different network structures [11], communication mechanism [12], self-propulsion and friction forces [13], and external fields such as fluid field [14], temperature field [15,16], potential fields [1], etc. These extensions significantly influence the dynamics of the system, leading to behaviors that are markedly different from the original model. Moreover, the connection between the kinetic Cucker-Smale model and the Euler-alignment system has been rigorously explored in recent literature. For the Euler-alignment system with pressure effects, Karper et al. [17] rigorously justified the hydrodynamic limit of the kinetic Cucker-Smale flocking model. Furthermore, Poyato and Soler [18] provided detailed analysis of a compressible Euler-type equation with singular commutator, which is derived from a hyperbolic limit of the kinetic Cucker-Smale model. In the pressure-less case, the derivation from the kinetic Cucker-Smale model to the nonlocal Euler-alignment system was established by [19]. Recently, Fabisiak and Peszek [20] rigorously derived the macroscopic fractional Euler-alignment system from the kinetic Cucker-Smale equation without performing any hydrodynamic limit.

    The well-posedness of solution is a fundamental concept in the theory of partial differential equations. Previous works [21,22,23] have established the well-posedness of weak and strong solution to the kinetic Cucker-Smale model without external potentials. Recently, Jin [24] developed a unified framework to establish the well-posedness of the model with or without noise. In this paper, the global well-posedness of the noisy version of kinetic Cucker-Smale model with harmonic potential field is studied. We prove the global nonnegativity, existence, and uniqueness of the strong solution for the system (1.1). Our approach is based on a combination of weighted Sobolev spaces and approximation schemes, which have been shown to be effective in dealing with inherent nonlinear and nonlocal problems in [25,26,27].

    Notation: We denote the usual Lp norms on Ω by f(t)Lp:=f(t)Lp(Ω), and the ith element L[f]i of the vector L[f] by

    L[f]i=Ωφ(|xy|)f(t,y,vi)(vivi)dydv.

    Then, we construct three special weighted Sobolev spaces with power ω(x,v)=(1+x2+v2)12:

    L1ω(Ω):={f(t,x,v):fL1ω(Ω)<},H1ω(Ω):={f(t,x,v):fH1ω(Ω)<},X(Ω):={f(t,x,v):fX(Ω)<},fH1ω:=fL2ω+xfL2ω+vfL2ω,fX:=ωvfL2ω+ωxfL2ω+ω2fL2ω,

    where

    fL1ω:=fL1ω(Ω)=Ωωf(t,x,v)dxdv,fL2ω:=fL2ω(Ω)=(Ωω2f2(t,x,v)dxdv)12.

    In the rest of the paper, we denote Cd and Cd,σ as positive constants while subscripts are used to indicate specific dependencies of such constants.

    Definition 1.1. Let 0f(t,x,v)C([0,);L1ω(Ω)). The function f(t,x,v) is a weak solution to system (1.1) if

    tf+vxfxU(x)vf+v(L[f]f)=σΔvf,inD([0,+)×Ω).

    We say f(t,x,v) is a strong solution if f(t,x,v) is a weak solution and f(t,x,v)C([0,);H1ω(Ω)).

    Now, our main results are stated as follows.

    Theorem 1.1. Assume initial datum f0(x,v)X(Ω)L1ω(Ω). Then, the system (1.1) admits a unique strong solution in sense of Definition 1.1.

    Remark 1.1. In this paper, we consider a special potential function U=12|x|2. In fact, similar to the study in [1], the potential U can be extended to a more general case: assume that the potential function U satisfies the following conditions:

    a2|x|2U(x)A2|x|2,a|x||U(x)|A|x|,xR3,0<aA. (1.3)

    One can obtain the existence and uniqueness of a strong solution to the system (1.1) with (1.3).

    The existence of a strong solution is constructed in the weighted Sobolev space H1ω(Ω). However, to find the Cauchy sequences in this space, we need to give extra estimates for terms such as ωvfn(t)2L2ω and ωxfn(t)2L2ω. For this purpose, we construct the weighted Sobolev space X(Ω) and establish the a priori estimate for the preparation; see Proposition 3.1.

    The rest of this paper is organized as follows. In Section 2, we establish a priori estimates of the system (1.1) by taking advantage of three special weighted Sobolev spaces. In Section 3, we first prove the local existence and uniqueness of the strong solution to system (1.1) by an iteration scheme and extend the local existence to the global one.

    This section is devoted to the a priori estimates for the system (1.1). Directly integrating system (1.1) over [0,t]×Ω gives that any smooth solution of it satisfies the following conservation law: Ωf0(x,v)dxdv=Ωf(t,x,v)dxdv=:Ωf(t)dxdv. Therefore, w.o.l.g., we assume Ωf0(x,v)dxdv=1 in the rest of paper. We first give the following a priori estimates for f(t)L1ω and f(t)L2ω, which are better integrabilities of f with large v and x.

    Lemma 2.1. Assume the function f(t,x,v) is a smooth solution to system (1.1) with initial datum 0f0X(Ω)L1ω(Ω) and Ωf0(x,v)dxdv=1. Then, for  t0, we have

    (1) Ωf(t)dxdv=1, f(t)L1=1, and f(t)0;

    (2) f(t)L1ωC(t);

    (3) f(t)2L2ω+2σt0vf(τ)2L2ωdτCexp(t0C(τ)dτ);

    where C and C(t) are positive constant and positive continuous functions of t both depending on σ,d and the weighted norms of the initial datum f0.

    Proof. (1) We multiply system (1.1) by sgn(f) and integrate it over Ω to obtain

    ddtf(t)L1=0,

    which implies f(t)L1=f0L1=1,t0. Note that

    (2.1)
    (2.2)

    We subtract system (2.1) from (2.2) to obtain , which gives .

    (2) Multiplying system (1.1) by and integrating by parts over leads to

    By applying Grönwall's lemma, we obtain

    where is the function of depending on , , and .

    (3) We multiply system (1.1) by to obtain

    (2.3)

    Then, we integrate system (2.3) by parts over to obtain

    (2.4)

    where

    Applying Grönwall's lemma to system (2.4), we obtain

    where is the function of depending on , , and .

    Now we derive the estimates in the weighted Sobolev spaces. The first weighted space is prepared for the strong solution, while the second space is constructed to estimate the approximate solutions.

    Proposition 2.1. Assume the function is a smooth solution to system (1.1) with initial datum satisfying the condition of Lemma 2.1. Then, for , we have

    (1) ;

    (2) ;

    where and are positive constant and positive continuous functions of both depending on and the weighted norms of the initial datum .

    Proof. (1) Applying to system (1.1) gives

    (2.5)

    Multiplying system (2.5) by leads to

    (2.6)

    Then we integrate system (2.6) by parts over to obtain

    (2.7)

    By applying to system (1.1), we obtain

    (2.8)

    Multiplying system (2.8) by gives

    (2.9)

    Then, we integrate system (2.9) by parts over to obtain

    (2.10)

    Now we estimate the last term on the right hand side of system (2.10).

    (2.11)

    where we have used the -Young's inequality and the following facts:

    and

    Combining system (2.11) with (2.10) yields

    (2.12)

    Due to the term which appeared in system (2.12), we need to analyze it in detail to close the a priori estimate in . Similarly to the estimate of system (2.4), we multiply system (1.1) by and integrate it over to have

    (2.13)

    Adding up systems (2.4), (2.7), (2.12), and (2.13), we can get

    By Grönwall's lemma and the estimate in Lemma 2.1, we obtain

    where depends on , , and .

    (2) We estimate the first term of . Multiplying system (2.5) by gives

    Integrating it over , we can get

    Adding up the above estimates leads to

    (2.14)

    Then, we estimate the second term of . Multiplying system (2.8) by leads to

    Similarly, we integrate it over as follows:

    where we use the fact ;

    Similar to the way of estimating the term , we have

    Adding up the above estimates for the integrals , we obtain

    (2.15)

    Finally, we consider the last term of . Similar to the estimate of system (2.4), we multiply system (2.5) by and integrate over to have

    (2.16)

    Adding up systems (2.14)–(2.16), one has

    Applying Grönwall's lemma and the estimate in Lemma 2.1, we obtain

    where depends on , , and . Therefore, we finish the proof.

    In this section, we prove the local existence and uniqueness of the strong solution to system (1.1) by constructing a sequence of approximate solutions and extend the local existence to the global. First, let us recall a Grönwall-type lemma in [28].

    Lemma 3.1. Let and be the sequence of the nonnegative continuous functions on . Assume that satisfies

    where , and are nonnegative constants.

    If there exists a constant depending on such that

    If there exists a constant depending on such that

    To begin with, we construct the following iteration scheme on finite time .

    (3.1)

    for . We define a sequence of approximate solutions as the solution to the above iterative system (3.1) by induction.

    Initial step (): we set

    With this, we solve the initial value problem for the Cucker-Smale model with external potential field and noise:

    subject to

    Inductive step: Suppose we have the sequence of smooth approximate solutions . Then, we can solve the following model:

    (3.2)

    subject to initial datum:

    Thus, we can construct the smooth function from . The solvability of system (3.2) is similar to that in the appendix of reference [29]. Therefore, the sequence is well-defined.

    Paralleling to the a priori estimate for the solution in Section 2, we can establish the uniform energy estimates for the approximate sequence in the weighted Sobolev spaces.

    Lemma 3.2. Let . Assume the function is a smooth solution to system (3.1) with the initial datum satisfying and . Then, for , we have

    (1) , , and ;

    (2) ;

    (3) ;

    where denotes the positive constant only depending on , and the weighted norms of the initial datum .

    Proof. (1) The results are obvious.

    (2) We multiply system (3.1) by and integrate it over to obtain

    (3.3)

    Then we integrate system (3.3) over to obtain

    (3.4)

    Applying Lemma 3.1 to system (3.4), one gets

    where the positive constant depends on , , and . Then, for any given , there exists a positive depending , and the weighted norms of the initial datum such that for all ,

    (3) Following the way of the computation for equations (2.3) and (2.4), we can conclude that .

    Proposition 3.1. Let . Assume the function is a smooth solution to system (3.1) with initial datum satisfying the condition of Lemma 3.2. Then, for , we have

    (1) ;

    (2) ;

    where denotes positive constant only depending on , and the weighted norms of the initial datum .

    Proof. Following the similar proof of Proposition 2.1 in Section 2, we can obtain the parallel results.

    Next, we show that the approximate solution is the Cauchy sequence in , where . Setting , it follows from system (3.1) that

    (3.5)

    It is obvious to see that

    Proposition 3.2. Assume that initial datum , , and . For any given positive small time , we have

    where denotes the positive constant only depending on , and the weighted norms of the initial datum .

    Furthermore, there exists a limit function such that

    Proof. For arbitrary and , multiplying system (3.5) by and integrating it over by parts lead to

    (3.6)

    where depends on , and the weighed norms of the initial datum and , since we used the conclusion of Lemma 3.2. Then, we integrate system (3.6) to obtain

    Using Lemma 3.1, we can derive that

    Then, for any given small time , we have

    (3.7)

    where denotes the positive constant only depending on , and the weighted norms of the initial datum . This means that is the Cauchy sequence in . Moreover, there exists a limit function such that

    Proposition 3.3. Assume that the initial datum satisfies the condition of Proposition 3.2. For any given positive small time , we have

    (3.8)
    (3.9)
    (3.10)

    where denotes the positive constant only depending on , and the weighted norms of the initial datum . Furthermore, there exists a limit function such that

    Proof. (1) Multiplying system (3.5) by leads to

    (3.11)

    Similar to the way to estimate system (2.3), we only need to estimate two extra terms and . Note that

    where , and we have used the -Young's inequality.

    Thus, by integrating system (3.11) over and using the estimate of Lemma 3.2 and Proposition 3.1, we have

    (3.12)

    Here, depends on , and weighed norms of initial datum.

    By applying Grönwall's lemma to system (3.12) on and using the conclusion of Proposition 3.2, we obtain

    (3.13)

    Then for any given small time , we have

    (3.14)

    Hence, we obtain the estimate system (3.8).

    (2) Multiplying system (3.5) by and integrating it over leads to

    which is similar in form to the result of system (3.12). Hence, we obtain estimate system (3.9).

    (3) Applying to system (3.5) gives

    (3.15)

    We multiply system (3.15) by and integrate it over . Comparing to system (2.7), we only need to estimate two extra terms and . Note that

    (3.16)

    where we have used the -Young's inequality.

    (3.17)

    Thus, similar to system (2.7), we use systems (3.16) and (3.17) to obtain

    (3.18)

    Applying to system (3.5) gives

    (3.19)

    We multiply system (3.19) by and integrate it over . Comparing to system (2.9), we only estimate the extra terms , , and .

    (3.20)

    Note that

    (3.21)

    where we have used the -Young's inequality.

    (3.22)

    Thus, similar to system (2.12), we use systems (3.20)–(3.22) to obtain

    (3.23)

    Adding up systems (3.12), (3.18), and (3.23), and applying Lemma 3.2-Proposition 3.2 and Proposition 3.3(2), we obtain

    Similar to systems (3.13) and (3.14), for any given positive small time , we have

    (3.24)

    which is system (3.10). This means that the approximate solution is the Cauchy sequence in Thus, it converges strongly to the limit function as

    With the help of estimates system (3.7) in Proposition 3.2 and (3.24) in Proposition 3.3, we conclude that there exists a constant depending on , and weighed norms of initial datum, such that for any given positive small time ,

    Therefore, the limit function of Cauchy sequence is a local strong solution to the system (1.1). The uniqueness of the solution can be derived easily. Let and be the two strong solutions above corresponding to the same initial datum . Set

    Then, by the same argument as in Lemma 3.2-Proposition 3.3, satisfies Grönwall's inequality:

    and the standard Grönwall's lemma implies that

    which gives the uniqueness of the local solution.

    When initial datum is smooth, the limit function from Proposition 3.2–Proposition 3.3 is the unique local smooth solution to system (1.1). Combining with Lemma 2.1 and Proposition 2.1, one can extend the local smooth solution to be global-in-time. Hence, we obtain the global smooth solution.

    When initial datum is not smooth, we first mollify the initial datum by convolution, i.e.,

    where is the standard mollifier. Then, we consider the following modified system

    Following the basic idea about the proof of Theorem 3.1 and Theorem 3.2 in [24], we can also prove that there exists a sequence , with , such that

    and satisfies system (1.1). Hence, the limit function is the desired unique strong solution.

    All authors contributed equally to the study and the writing of the manuscript.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This work is supported by the Natural Science Foundation of China (No.12001097), Natural Science Foundation of Shanghai Municipality (No. 22ZR1402300), and AI-Enhanced Research Program of Shanghai Municipal Education Commission (No. SMEC-AI-DHUY-01).

    The authors declare there is no conflict of interest.



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