Knowledge graph embedding by fusing multimodal content via cross-modal learning

Shi Liu; Kaiyang Li; Yaoying Wang; Tianyou Zhu; Jiwei Li; Zhenyu Chen; Shi Liu; Kaiyang Li; Yaoying Wang; Tianyou Zhu; Jiwei Li; Zhenyu Chen

doi:10.3934/mbe.2023634

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14180-14200. doi: 10.3934/mbe.2023634

Previous Article Next Article

Research article

Knowledge graph embedding by fusing multimodal content via cross-modal learning

Big Data Center of State Grid Corporation, Beijing 100052, China

Academic Editor: Yang Kuang

Received: 01 February 2023 Revised: 27 April 2023 Accepted: 21 May 2023 Published: 26 June 2023

Knowledge graph embedding aims to learn representation vectors for the entities and relations. Most of the existing approaches learn the representation from the structural information in the triples, which neglects the content related to the entity and relation. Though there are some approaches proposed to exploit the related multimodal content to improve knowledge graph embedding, such as the text description and images associated with the entities, they are not effective to address the heterogeneity and cross-modal correlation constraint of different types of content and network structure. In this paper, we propose a multi-modal content fusion model (MMCF) for knowledge graph embedding. To effectively fuse the heterogenous data for knowledge graph embedding, such as text description, related images and structural information, a cross-modal correlation learning component is proposed. It first learns the intra-modal and inter-modal correlation to fuse the multimodal content of each entity, and then they are fused with the structure features by a gating network. Meanwhile, to enhance the features of relation, the features of the associated head entity and tail entity are fused to learn relation embedding. To effectively evaluate the proposed model, we compare it with other baselines in three datasets, i.e., FB-IMG, WN18RR and FB15k-237. Experiment result of link prediction demonstrates that our model outperforms the state-of-the-art in most of the metrics significantly, implying the superiority of the proposed method.

Keywords:

Citation: Shi Liu, Kaiyang Li, Yaoying Wang, Tianyou Zhu, Jiwei Li, Zhenyu Chen. Knowledge graph embedding by fusing multimodal content via cross-modal learning[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14180-14200. doi: 10.3934/mbe.2023634

Related Papers:

[1]	Giuseppe Maria Coclite, Lorenzo di Ruvo . A singular limit problem for conservation laws related to the Kawahara-Korteweg-de Vries equation. Networks and Heterogeneous Media, 2016, 11(2): 281-300. doi: 10.3934/nhm.2016.11.281
[2]	Hyeontae Jo, Hwijae Son, Hyung Ju Hwang, Eun Heui Kim . Deep neural network approach to forward-inverse problems. Networks and Heterogeneous Media, 2020, 15(2): 247-259. doi: 10.3934/nhm.2020011
[3]	Anya Désilles, Hélène Frankowska . Explicit construction of solutions to the Burgers equation with discontinuous initial-boundary conditions. Networks and Heterogeneous Media, 2013, 8(3): 727-744. doi: 10.3934/nhm.2013.8.727
[4]	Tong Yan . The numerical solutions for the nonhomogeneous Burgers' equation with the generalized Hopf-Cole transformation. Networks and Heterogeneous Media, 2023, 18(1): 359-379. doi: 10.3934/nhm.2023014
[5]	Jinyi Sun, Weining Wang, Dandan Zhao . Global existence of 3D rotating magnetohydrodynamic equations arising from Earth's fluid core. Networks and Heterogeneous Media, 2025, 20(1): 35-51. doi: 10.3934/nhm.2025003
[6]	Guillermo Reyes, Juan-Luis Vázquez . The Cauchy problem for the inhomogeneous porous medium equation. Networks and Heterogeneous Media, 2006, 1(2): 337-351. doi: 10.3934/nhm.2006.1.337
[7]	Caihong Gu, Yanbin Tang . Global solution to the Cauchy problem of fractional drift diffusion system with power-law nonlinearity. Networks and Heterogeneous Media, 2023, 18(1): 109-139. doi: 10.3934/nhm.2023005
[8]	Bendong Lou . Self-similar solutions in a sector for a quasilinear parabolic equation. Networks and Heterogeneous Media, 2012, 7(4): 857-879. doi: 10.3934/nhm.2012.7.857
[9]	Yannick Holle, Michael Herty, Michael Westdickenberg . New coupling conditions for isentropic flow on networks. Networks and Heterogeneous Media, 2020, 15(4): 605-631. doi: 10.3934/nhm.2020016
[10]	Elisabeth Logak, Isabelle Passat . An epidemic model with nonlocal diffusion on networks. Networks and Heterogeneous Media, 2016, 11(4): 693-719. doi: 10.3934/nhm.2016014

Abstract

1. Introduction

The equation:

$\begin{equation} \begin{cases} \partial_t u + \partial_x f(u)-\beta^2 \partial_{x}^2 u+\delta \partial_{x}^3 u+\kappa u +\gamma^2\vert u\vert u = 0, &\quad 0 < t < T, \quad x\in \mathbb{R},\\ u(0,x) = u_0(x), &\quad x\in \mathbb{R}, \end{cases} \end{equation}$

(1.1)

was originally derived in ^[14,17] with $f(u) = au^2$ focusing on microbubbles coated by viscoelastic shells. These structures are crucial in ultrasound diagnosis using contrast agents, and the dynamics of individual coated bubbles are explored, taking into account nonlinear competition and dissipation factors such as dispersion, thermal effects, and drag force.

The coefficients $\beta^2$ , $\delta$ , $\kappa$ , and $\gamma^2$ are related to the dissipation, the dispersion, the thermal conduction dissipation, and to the drag force, repsctively.

If $\kappa = \gamma = 0$ , we obtain the Kudryashov-Sinelshchikov ^[18] Korteweg-de Vries-Burgers ^[3,20] equation

$\begin{equation} \partial_t u +a \partial_x u^2-\beta^2 \partial_{x}^2 u+\delta \partial_{x}^3 u = 0, \end{equation}$

(1.2)

that models pressure waves in liquids with gas bubbles, taking into account heat transfer and viscosity. The mathematical results on Eq (1.2) are the following:

● analysis of exact solutions in ^[13],

● existence of the traveling waves in ^[2],

● well-posedness and asymptotic behavior in ^[7,11].

If $\beta = 0$ , we derive the Korteweg-de Vries equation:

$\begin{equation} \partial_t u +a \partial_x u^2+\delta \partial_{x}^3 u = 0, \end{equation}$

(1.3)

which describes surface waves of small amplitude and long wavelength in shallow water. Here, $u(t, x)$ represents the wave height above a flat bottom, $x$ corresponds to the distance in the propagation direction, and $t$ denotes the elapsed time. In ^{[4,6,10,12,15,16]}, the completele integrability of Eq (1.3) and the existence of solitary wave solutions are proved.

Through the manuscript, we will assume

● on the coefficients

$\begin{equation} \beta,\,\delta,\,\kappa,\,\gamma\in \mathbb{R}, \quad \beta,\,\delta,\,\gamma\ne0; \end{equation}$

(1.4)

● on the flux $f$ , one of the following conditions:

$\begin{align} &f(u) = a u^2+bu^3, \end{align}$

(1.5)

$\begin{align} &f\in C^1( \mathbb{R}), \quad \vert f'(u)\vert\le C_0\left(1+\vert u\vert\right), \quad u\in \mathbb{R}, \end{align}$

(1.6)

for some positive constant $C_0$ ;

● on the initial value

$\begin{equation} u_0\in H^1( \mathbb{R}). \end{equation}$

(1.7)

The main result of this paper is the following theorem.

Theorem 1.1. Assume Eqs (1.5)–(1.7). For fixed $T > 0$ , there exists a unique distributional solution $u$ of Eq (1.1), such that

$\begin{equation} \begin{split} u \in&\, L^{\infty}(0,T;H^1( \mathbb{R}))\cap L^4(0,\,T;W^{1,\,4}( \mathbb{R}))\cap L^6(0,\,T;W^{1,\,6}( \mathbb{R}))\\ \partial_{x}^2 u\in&\, L^2((0,\,T)\times \mathbb{R}). \end{split} \end{equation}$

(1.8)

Moreover, if $u_1$ and $u_2$ are solutions to Eq (1.1) corresponding to the initial conditions $u_{1, 0}$ and $u_{2, 0}$ , respectively, it holds that:

$\begin{equation} \left\| {u_1(t,\cdot)-u_2(t,\cdot)} \right\|_{L^2( \mathbb{R})}\le e^{C(T)t}\left\| {u_{1,0}-u_{2,0}} \right\|_{L^2( \mathbb{R})}, \end{equation}$

(1.9)

for some suitable $C(T) > 0$ , and every, $0\le t\le T$ .

Observe that Theorem 1.1 gives the well-posedness of (1.1), without conditions on the constants. Moreover, the proof of Theorem 1.1 is based on the Aubin-Lions Lemma ^[5,21]. The analysis of Eq (1.1) is more delicate than the one of Eq (1.2) due to the presence of the nonlinear sources and the very general assumptions on the coefficients.

The structure of the paper is outlined as follows. Section 2 is dedicated to establishing several a priori estimates for a vanishing viscosity approximation of Eq (1.1). These estimates are crucial for proving our main result, which is presented in Section 3.

2. Vanishing viscosity approximation

To establish existence, we utilize a vanishing viscosity approximation of equation (1.1), as discussed in ^[19]. Let $0 < \varepsilon < 1$ be a small parameter, and denote by $u_\varepsilon\in C^\infty([0, T)\times \mathbb{R})$ the unique classical solution to the following problem ^[1,9]:

$\begin{equation} \begin{cases} \partial_t u_\varepsilon + \partial_x f( u_\varepsilon)-\beta^2 \partial_{x}^2 u_\varepsilon+\delta \partial_{x}^3 u_\varepsilon+\kappa u\\ \qquad\quad +\gamma^2\vert u\vert u = - \varepsilon \partial_{x}^4 u_\varepsilon, &\quad 0 < t < T, \quad x\in \mathbb{R},\\ u_{ \varepsilon}(0,x) = u_{ \varepsilon,\,0}(x), &\quad x\in \mathbb{R}, \end{cases} \end{equation}$

(2.1)

where $u_{ \varepsilon, \, 0}$ is a $C^{\infty}$ approximation of $u_0$ , such that

$\begin{equation} \left\| {u_{ \varepsilon,\,0}} \right\|_{H^1( \mathbb{R})}\le \left\| {u_0} \right\|_{H^1( \mathbb{R})}. \end{equation}$

(2.2)

Let us prove some a priori estimates on $u_\varepsilon$ , denoting with $C_0$ constants which depend only on the initial data, and with $C(T)$ the constants which depend also on $T$ .

We begin by proving the following lemma:

Lemma 2.1. Let $T > 0$ be fixed. There exists a constant $C(T) > 0$ , which does not depend on $\varepsilon$ , such that

$\begin{equation} \begin{split} \left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}&+2\gamma^2e^{\vert\kappa\vert t}\displaystyle {\int}_{0}^{t}\!\!\displaystyle {\int}_{ \mathbb{R}}e^{-\vert\kappa\vert s} u_\varepsilon^2\vert u_\varepsilon\vert dsdx\\ &+2\beta^2e^{\vert\kappa\vert t}\displaystyle {\int}_{0}^{t}e^{-\vert\kappa\vert s}\left\| { \partial_x u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})} ds\\ &+2 \varepsilon e^{\vert\kappa\vert t}\displaystyle {\int}_{0}^{t}e^{-\vert\kappa\vert s}\left\| { \partial_{x}^2 u_\varepsilon(s,\cdot)} \right\|^2_{L^2(R)}\le C(T), \end{split} \end{equation}$

(2.3)

for every $0\le t\le T$ .

Proof. For $0\le t\le T$ . Multiplying equations (2.1) by $2 u_\varepsilon$ , and integrating over $\mathbb{R}$ yields

$\begin{align*} \frac{d}{dt}&\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} = 2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_t u_\varepsilon dx\\ = &\underbrace{-2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon f'( u_\varepsilon) \partial_x u_\varepsilon dx}_{ = 0}+2\beta^2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_{x}^2 u_\varepsilon dx-2\delta\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_{x}^3 u_\varepsilon dx \\ &-\kappa\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^2 dx-2 \varepsilon\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_{x}^4 u_\varepsilon dx \\ = &-2\beta^2\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\delta\displaystyle {\int}_{ \mathbb{R}} \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx\\ &-\kappa\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^2 dx+2 \varepsilon\displaystyle {\int}_{ \mathbb{R}} \partial_x u_\varepsilon \partial_{x}^3 u_\varepsilon dx\\ = &-2\beta^2\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-\kappa\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &-2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^2 dx-2 \varepsilon\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

Thus, it follows that

$\begin{align*} \frac{d}{dt}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+&2\beta^2\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &+2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^2 dx+2 \varepsilon\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ = &\kappa\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\le \vert\kappa\vert \left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

Therefore, applying the Gronwall's lemma and using Eq (2.2), we obtain

$\begin{align*} \left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}&+2\beta^2 e^{\vert\kappa\vert t}\displaystyle {\int}_{0}^{t}e^{-\vert\kappa\vert s}\left\| { \partial_x u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds\\ &+ 2\gamma^2e^{\vert\kappa\vert t}\displaystyle {\int}_{0}^t\!\!\displaystyle {\int}_{ \mathbb{R}}e^{-\vert\kappa\vert t}\vert u_\varepsilon\vert u_\varepsilon^2 dsdx+2 \varepsilon\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &+2 \varepsilon e^{\vert\kappa\vert t}\displaystyle {\int}_{0}^{t}e^{-\vert\kappa\vert s} \left\| { \partial_{x}^2 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds\\ \le& C_0e^{\vert\kappa\vert t}\le C(T), \end{align*}$

which gives Eq (2.3). □

Lemma 2.2. Fix $T > 0$ and assume (1.5). There exists a constant $C(T) > 0$ , independent of $\varepsilon$ , such that

$\begin{align} \left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}&\le C(T), \end{align}$

(2.4)

$\begin{align} \left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\beta^2\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^2 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds& \end{align}$

(2.5)

$\begin{align} +2 \varepsilon\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^3 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds&\le C(T),\\ \displaystyle {\int}_{0}^{t}\left\| { \partial_x u_\varepsilon(s,\cdot)} \right\|^4_{L^4( \mathbb{R})} ds&\le C(T), \end{align}$

(2.6)

holds for every $0\le t\le T$ .

Proof. Let $0\le t\le T$ . Consider $A, \, B$ as two real constants, which will be specified later. Thanks to Eq (1.5), multiplying Eq (2.1) by

$\begin{equation*} -2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3, \end{equation*}$

we have that

$\begin{align} &\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_t u_\varepsilon+ 2a\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) u_\varepsilon \partial_x u_\varepsilon\\ &\qquad\quad+3b\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) u_\varepsilon^2 \partial_x u_\varepsilon-\beta^2\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_{x}^2 u_\varepsilon \\ &\qquad\quad+\delta\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_{x}^3 u_\varepsilon+\kappa\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) u_\varepsilon\\ &\qquad\quad+\gamma^2\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right)\vert u_\varepsilon\vert u_\varepsilon = - \varepsilon\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_{x}^4 u_\varepsilon. \end{align}$

(2.7)

Observe that

$\begin{align*} &\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_t u_\varepsilon dx\\ &\qquad = \frac{d}{dt}\left(\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\frac{A}{3}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx +\frac{B}{4}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\right),\\ &2a\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) u_\varepsilon \partial_x u_\varepsilon dx = -4a\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx,\\ &3b\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) u_\varepsilon^2 \partial_x u_\varepsilon dx = -6b\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2 \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx,\\ &{-\beta^2\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_{x}^2 u_\varepsilon dx}\\ &\qquad = 2\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2A\beta^2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon( \partial_x u_\varepsilon)^2 dx +3B\beta^2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_x u_\varepsilon)^2dx, \\ &\delta\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_{x}^3 u_\varepsilon dx = -2A\delta\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx -3B\delta\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2 \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx, \\ &\kappa\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) u_\varepsilon dx\\ &\qquad = {2\kappa\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+{A\kappa}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3 dx +{B\kappa}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx,}\\ &\gamma^2\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right)\vert u_\varepsilon\vert u_\varepsilon dx\\ &\qquad = -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx +A\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u\vert u_\varepsilon^3dx +B\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u^4 dx,\\ &- \varepsilon\displaystyle {\int}_{ \mathbb{R}}\left(-2 \partial_{x}^2 u_\varepsilon+A u_\varepsilon^2+B u_\varepsilon^3\right) \partial_{x}^4 u_\varepsilon dx\\ &\qquad = -2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2A \varepsilon\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_x u_\varepsilon{ \partial_{x}^3 u_\varepsilon} dx+3B \varepsilon\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2 \partial_x u_\varepsilon \partial_{x}^3 u_\varepsilon dx\\ &\qquad = -2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-A \varepsilon\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^3 dx -6B \varepsilon\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon( \partial_x u_\varepsilon)^2 \partial_{x}^2 u_\varepsilon dx\\ &\qquad\quad -3B \varepsilon\left\| { u_\varepsilon(t,\cdot) \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad = -2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-A \varepsilon\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^3 dx +2B \varepsilon\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4dx\\ &\qquad\quad -3B \varepsilon\left\| { u_\varepsilon(t,\cdot) \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

Therefore, an integration on $\mathbb{R}$ gives

$\begin{align*} &\frac{d}{dt}\left(\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\frac{A}{3}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx +\frac{B}{4}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\right)\\ &\qquad\quad +\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad = -\left(4a+A\delta\right)\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx -3\left(2b+B\delta\right)\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2 \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx\\ &\qquad\quad -2A\beta^2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon( \partial_x u_\varepsilon)^2 dx -3B\beta^2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_x u_\varepsilon)^2dx\\ &\qquad\quad -\kappa\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-\frac{A\kappa}{3}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3 dx -\frac{B\kappa}{4}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\\ &\qquad\quad +2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx {-A\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^3dx-B\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^4 dx}\\ &\qquad\quad -A \varepsilon\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^3 dx +2B \varepsilon\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4dx-3B \varepsilon\left\| { u_\varepsilon(t,\cdot) \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

Taking

$\begin{equation*} \left(A,\,B\right) = \left(-\frac{4a}{\delta},\,-\frac{2b}{\delta}\right), \end{equation*}$

we get

$\begin{align} &\frac{d}{dt}\left(\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-\frac{4a}{3\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx -\frac{b}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\right)\\ &\qquad\quad +2\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad = \frac{8a\beta^2}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon( \partial_x u_\varepsilon)^2 dx +\frac{6b\beta^2}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_x u_\varepsilon)^2dx\\ &\qquad\quad -\kappa\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\frac{4a\kappa}{3\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3 dx +\frac{b\kappa}{2}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\\ &\qquad\quad +2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx {+\frac{4a\gamma^2}{\delta}\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^3dx +\frac{2b\gamma^2}{\delta}\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon^4 dx} \\ &\qquad\quad +\frac{4a \varepsilon}{\delta}\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^3 dx-\frac{4b \varepsilon}{\delta}\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4dx+\frac{6b \varepsilon}{\delta}\left\| { u_\varepsilon(t,\cdot) \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align}$

(2.8)

Since $0 < \varepsilon < 1$ , due to the Young inequality and (2.3),

$\begin{align*} &\frac{8a\beta^2}{\delta}\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert( \partial_x u_\varepsilon)^2 dx\\ &\qquad\le 4\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_x u_\varepsilon)^2 dx +\frac{4a^2\beta^4}{\delta^2}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le 4\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\frac{4a^2\beta^4}{\delta^2}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C_0\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right)\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})},\\ &\left\vert\frac{6b\beta^2}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_x u_\varepsilon)^2dx\le \left\vert\frac{6b\beta^2}{\delta}\right\vert\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})},\\ &\vert\frac{4a\kappa}{3\delta}\vert\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert^3 dx\le \left\vert\frac{4a\kappa}{3\delta}\right\vert\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C(T)\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})},\\ &\left\vert\frac{b\kappa}{2}\right\vert\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\le \left\vert\frac{b\kappa}{2}\right\vert\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}, \\ &2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx{\le}2\displaystyle {\int}_{ \mathbb{R}}\left\vert\frac{\gamma^2\vert u_\varepsilon\vert u_\varepsilon}{\beta}\right\vert\left\vert\beta \partial_{x}^2 u_\varepsilon\right\vert dx\\ &\qquad\le\frac{\gamma^4}{\beta^2}\displaystyle {\int}_{ \mathbb{R}}{ u_\varepsilon}^4dx +\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad \le\frac{\gamma^4}{\beta^2}\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0.T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} +\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})},\\ &\left\vert\frac{4a\gamma^2}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}}\vert { u_\varepsilon}\vert \vert u_\varepsilon\vert^3dx = \left\vert\frac{4a\gamma^2}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\\ &\qquad\le \left\vert\frac{4a\gamma^2}{\delta}\right\vert\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} \le C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}, \\ &\left\vert\frac{2b\gamma^2}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert { u_\varepsilon}^4 dx\le \left\vert\frac{2b\gamma^2}{\delta}\right\vert\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,\,T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C(T)\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,\,T)\times \mathbb{R})},\\ &\left\vert\frac{4a \varepsilon}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}}\vert \partial_x u_\varepsilon\vert^3 dx\le\left\vert\frac{4a \varepsilon}{\delta}\right\vert\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} +\left\vert\frac{4a \varepsilon}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4 dx\\ &\qquad \le \left\vert\frac{4a}{\delta}\right\vert\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} +\left\vert\frac{4a \varepsilon}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4 dx. \end{align*}$

It follows from Eq (2.8) that

$\begin{align} &\frac{d}{dt}\left(\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-\frac{4a}{3\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx -\frac{b}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\right)\\ &\qquad\quad +\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C_0\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right)\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+C(T)\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\\ &\qquad\quad +C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+C(T)\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\\ &\qquad\quad+C_0 \varepsilon\displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4 dx +C_0 \varepsilon\left\| { u_\varepsilon(t,\cdot) \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\quad {+C_0\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}}. \end{align}$

(2.9)

[8, Lemma 2.3] says that

$\begin{equation} \displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^4 dx\le 9\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_{x}^2 u_\varepsilon)^2 dx\le 9\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,\,T)\times \mathbb{R})}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation}$

(2.10)

Moreover, we have that

$\begin{equation} \left\| { u_\varepsilon(t,\cdot) \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} = \displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_{x}^2 u_\varepsilon)^2 dx {\le}\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,\,T)\times \mathbb{R})}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation}$

(2.11)

Consequentially, by Eqs (2.9)–(2.11), we have that

$\begin{align*} &\frac{d}{dt}\left(\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-\frac{4a}{3\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx -\frac{b}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\right)\\ &\qquad\quad +\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C_0\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right)\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+C(T)\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\\ &\qquad\quad +C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+C(T)\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\\ &\qquad\quad +C_0 \varepsilon\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,\,T)\times \mathbb{R})}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} {+C_0\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}.}\notag \end{align*}$

An integration on $(0, t)$ and Eqs (2.2) and (2.3) give

$\begin{align*} &\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-\frac{4a}{3\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx -\frac{b}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx\\ &\qquad\quad +\beta^2\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^2 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds+2 \varepsilon\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^3 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds\\ &\qquad\le C_0\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right)\displaystyle {\int}_{0}^{t}\left\| { \partial_x u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds\\ &\qquad\quad+C(T)\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}t +C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}t+C(T)\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}t\\ &\qquad\quad +C_0 \varepsilon\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,\,T)\times \mathbb{R})}\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^2 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds {+C_0\displaystyle {\int}_{0}^{t}\left\| { \partial_x u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds}\\ &\qquad\le C(T)\left(1+\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})} +\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\right). \end{align*}$

Therefore, by Eq (2.3),

$\begin{align} &\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\beta^2\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^2 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds+2 \varepsilon\displaystyle {\int}_{0}^{t}\left\| { \partial_{x}^3 u_\varepsilon(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds\\ &\qquad\le C(T)\left(1+\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})} +\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\right)\\ &\qquad\quad +\frac{4a}{3\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^3dx +\frac{b}{\delta}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx \\ &\qquad\le C(T)\left(1+\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})} +\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\right)\\ &\qquad\quad +\left\vert\frac{4a}{3\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert^3dx +\left\vert\frac{b}{\delta}\right\vert\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx \\ &\qquad\le C(T)\left(1+\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})} +\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\right)\\ &\qquad\quad +\left\vert\frac{4a}{3\delta}\right\vert \left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { u_\varepsilon({t},\cdot)} \right\|^2_{L^2( \mathbb{R})} +\left\vert\frac{b}{\delta}\right\vert\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C(T)\left(1+\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})} +\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\right). \end{align}$

(2.12)

We prove Eq (2.4). Thanks to the Hölder inequality,

$\begin{equation*} u_\varepsilon^2(t,x) = 2\displaystyle {\int}_{-\infty}^{x} u_\varepsilon \partial_x u_\varepsilon dx\le 2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert\vert \partial_x u_\varepsilon\vert dx\le 2\left\| { u_\varepsilon(t,\cdot)} \right\|_{L^2( \mathbb{R})}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|_{L^2( \mathbb{R})}. \end{equation*}$

Hence, we have that

$\begin{equation} \left\| { u_\varepsilon(t,\cdot)} \right\|^4_{L^{\infty}( \mathbb{R})}\le 4\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation}$

(2.13)

Thanks to Eqs (2.3) and (2.12), we have that

$\begin{equation} \begin{split} &\left\| { u_\varepsilon} \right\|^4_{L^{\infty}((0,T)\times \mathbb{R})}\\ &\qquad\le C(T)\left(1+\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})} +\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\right). \end{split} \end{equation}$

(2.14)

Due to the Young inequality,

$\begin{align*} C(T)\left\| { u_\varepsilon} \right\|^3_{L^{\infty}((0,T)\times \mathbb{R})}\le& \frac{1}{2}\left\| { u_\varepsilon} \right\|^4_{L^{\infty}((0,T)\times \mathbb{R})}+C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})},\\ C(T)\left\| { u_\varepsilon} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\le& C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+C(T). \end{align*}$

By Eq (2.14), we have that

$\begin{equation*} \frac{1}{2}\left\| { u_\varepsilon} \right\|^4_{L^{\infty}((0,T)\times \mathbb{R})}-C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}-C(T){\le0}, \end{equation*}$

which gives Eq (2.4).

Equation (2.5) follows from Eqs (2.4) and (2.12).

Finally, we prove Eq (2.6). We begin by observing that, from Eqs (2.4) and (2.10), we have

$\begin{equation*} \left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^4_{L^4( \mathbb{R})}\le C(T)\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation*}$

An integration on $(0, t)$ and Eqs (2.5) give Eq (2.6). □

Lemma 2.3. Fix $T > 0$ and assume (1.6). There exists a constant $C(T) > 0$ , independent of $\varepsilon$ , such that Eq (2.4) holds. Moreover, we have Eqs (2.5) and (2.6).

Proof. Let $0\le t\le T$ . Multiplying Eq (2.1) by $-2 \partial_{x}^2 u_\varepsilon$ , an integration on $\mathbb{R}$ gives

$\begin{align*} \frac{d}{dt}&\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} = -2\displaystyle {\int}_{ \mathbb{R}} \partial_{x}^2 u_\varepsilon \partial_t u_\varepsilon dx\\ = &-2\displaystyle {\int}_{ \mathbb{R}}f'( u_\varepsilon) \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx-2\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} -2\delta\displaystyle {\int}_{ \mathbb{R}} \partial_{x}^2 u_\varepsilon \partial_{x}^3 u_\varepsilon dx\\ &-2\kappa\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon \partial_{x}^2 u_\varepsilon dx-2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx+2 \varepsilon\displaystyle {\int}_{ \mathbb{R}} \partial_{x}^2 u_\varepsilon \partial_{x}^4 u_\varepsilon dx\\ = &-2\displaystyle {\int}_{ \mathbb{R}}f'( u_\varepsilon) \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx -2\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &+2\kappa\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx-2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

Therefore, we have that

$\begin{equation} \begin{split} &\frac{d}{dt}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad = -2\displaystyle {\int}_{ \mathbb{R}}f'( u_\varepsilon) \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dx+2\kappa\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx. \end{split} \end{equation}$

(2.15)

Due Eqs (1.6) and (2.3) and the Young inequality,

$\begin{align*} &2\displaystyle {\int}_{ \mathbb{R}}\vert f'( u_\varepsilon)\vert\vert \partial_x u_\varepsilon\vert\vert \partial_{x}^2 u_\varepsilon\vert dx\le C_0\displaystyle {\int}_{ \mathbb{R}}\vert \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon\vert dx+C_0\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon \partial_x u_\varepsilon\vert\vert \partial_{x}^2 u_\varepsilon\vert dx\\ &\qquad = 2\displaystyle {\int}_{ \mathbb{R}}\left\vert\frac{C_0\sqrt{3} \partial_x u_\varepsilon}{2\beta}\right\vert\left\vert\frac{\beta \partial_{x}^2 u_\varepsilon}{\sqrt{3}}\right\vert dx+2\displaystyle {\int}_{ \mathbb{R}}\left\vert\frac{C_0\sqrt{3} u_\varepsilon \partial_x u_\varepsilon}{2\beta}\right\vert\left\vert\sqrt{3}\beta \partial_{x}^2 u_\varepsilon\right\vert dx\\ &\qquad\le C_0\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+C_0\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2( \partial_x u_\varepsilon)^2 dx +\frac{2\beta^2}{3}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C_0\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+C_0\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} +\frac{2\beta^2}{3}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C_0\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right)\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} +\frac{2\beta^2}{3}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})},\\ &2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_\varepsilon\vert u_\varepsilon \partial_{x}^2 u_\varepsilon dx\le 2\gamma^2\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^2\vert \partial_{x}^2 u_\varepsilon\vert dx = 2\displaystyle {\int}_{ \mathbb{R}}\left\vert\frac{\sqrt{3}\gamma^2 u_\varepsilon^2}{\beta}\right\vert\left\vert\frac{\beta \partial_{x}^2 u_\varepsilon}{\sqrt{3}}\right\vert dx\\ &\qquad\le \frac{3\gamma^4}{\beta^2}\displaystyle {\int}_{ \mathbb{R}} u_\varepsilon^4 dx + \frac{\beta^2}{3}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le\frac{3\gamma^4}{\beta^2}\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} + \frac{\beta^2}{3}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}+\frac{\beta^2}{3}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

It follows from Eq (2.15) that

$\begin{align*} &\frac{d}{dt}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\beta^2\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2 \varepsilon\left\| { \partial_{x}^3 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ &\qquad\le C_0\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right)\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}. \end{align*}$

Integrating on $(0, t)$ , by Eq (2.3), we have that

(2.16)

Thanks to Eqs (2.3), (2.13), and (2.16), we have that

$\begin{equation*} \left\| { u_\varepsilon} \right\|^4_{L^{\infty}((0,T)\times \mathbb{R})}\le C(T)\left(1+\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\right). \end{equation*}$

Therefore,

$\begin{equation*} \left\| { u_\varepsilon} \right\|^4_{L^{\infty}((0,T)\times \mathbb{R})}-C(T)\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}-C(T)\le 0, \end{equation*}$

which gives (2.4).

Equation (2.5) follows from (2.4) and (2.16), while, arguing as in Lemma 2.2, we have Eq (2.6).

□

Lemma 2.4. Fix $T > 0$ . There exists a constant $C(T) > 0$ , independent of $\varepsilon$ , such that

$\begin{equation} \displaystyle {\int}_{0}^{t}\left\| { \partial_x u_\varepsilon(s,\cdot)} \right\|^6_{L^6( \mathbb{R})}ds\le C(T), \end{equation}$

(2.17)

for every $0\le t\le T$ .

Proof. Let $0\le t\le T$ . We begin by observing that,

$\begin{equation} \displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^6 dx\le \left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^4_{L^{\infty}( \mathbb{R})}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation}$

(2.18)

Thanks to the Hölder inequality,

$\begin{align*} ( \partial_x u_\varepsilon(t,x))^2 = &2\displaystyle {\int}_{-\infty}^{x} \partial_x u_\varepsilon \partial_{x}^2 u_\varepsilon dy \le 2\displaystyle {\int}_{ \mathbb{R}}\vert \partial_x u_\varepsilon\vert\vert \partial_{x}^2 u_\varepsilon\vert dx\\ \le&2\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|_{L^2( \mathbb{R})}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

Hence,

$\begin{equation*} \left\| {u(t,\cdot)} \right\|^4_{L^{\infty}( \mathbb{R})}\le 4\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation*}$

It follows from Eq (2.18) that

$\begin{equation*} \displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^6 dx\le 4\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^4_{L^2( \mathbb{R})}\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation*}$

Therefore, by Eq (2.5),

$\begin{equation*} \displaystyle {\int}_{ \mathbb{R}}( \partial_x u_\varepsilon)^6 dx\le C(T)\left\| { \partial_{x}^2 u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation*}$

An integration on $(0, t)$ and Eq (2.5) gives (2.17). □

3. Proof of Theorem 1.1

This section is devoted to the proof of Theorem 1.1.

We begin by proving the following result.

Lemma 3.1. Fix $T > 0$ . Then,

$\begin{equation} {{the\ family\ }} \{ u_\varepsilon\}_{ \varepsilon > 0} {{ \ is\ compact\ in\ }} L^2_{loc}((0,T)\times \mathbb{R}) . \end{equation}$

(3.1)

Consequently, there exist a subsequence $\{ u_{\varepsilon_k} \}_{k\in \mathbb{N}}$ and $u\in L^2_{loc}((0, T)\times \mathbb{R})$ such that

$\begin{equation} u_{\varepsilon_k}\to u\ { in }\ L^2_{loc}((0,T)\times \mathbb{R}) {{\ and\ a.e.\ in\ }} (0,T)\times \mathbb{R} . \end{equation}$

(3.2)

Moreover, $u$ is a solution of Eq (1.1), satisfying Eq (1.8).

Proof. We begin by proving Eq (3.1). To prove Eq (3.1), we rely on the Aubin-Lions Lemma (see ^[5,21]). We recall that

$\begin{equation*} H^1_{loc}( \mathbb{R})\hookrightarrow\hookrightarrow L^2_{loc}( \mathbb{R})\hookrightarrow H^{-1}_{loc}( \mathbb{R}), \end{equation*}$

where the first inclusion is compact and the second one is continuous. Owing to the Aubin-Lions Lemma ^[21], to prove Eq (3.1), it suffices to show that

$\begin{align} & \{ u_\varepsilon\}_{ \varepsilon > 0} {\rm{\ is\ uniformly\ bounded\ in\ }} L^2(0,T;H^1_{loc}( \mathbb{R})) , \end{align}$

(3.3)

$\begin{align} & \{ \partial_t u_\varepsilon\}_{ \varepsilon > 0} {\rm{\ is\ uniformly\ bounded\ in\ }} L^2(0,T;H^{-1}_{loc}( \mathbb{R})) . \end{align}$

(3.4)

We prove Eq (3.3). Thanks to Lemmas 2.1–2.3,

$\begin{equation*} \left\| { u_\varepsilon(t,\cdot)} \right\|^2_{H^1( \mathbb{R})} = \left\| { u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\le C(T). \end{equation*}$

Therefore,

$\begin{equation*} \{ u_\varepsilon\}_{ \varepsilon > 0} {\rm{\ is\ uniformly\ bounded\ in }}\ L^{\infty}(0,T;H^{1}( \mathbb{R})) , \end{equation*}$

which gives Eq (3.3).

We prove Eq (3.4). Observe that, by Eq (2.1),

$\begin{equation*} \partial_t u_\varepsilon = - \partial_x\left( G( u_\varepsilon)\right)-f'( u_\varepsilon) \partial_x u_\varepsilon-\kappa u_\varepsilon -\gamma^2\vert u_\varepsilon\vert u_\varepsilon, \end{equation*}$

where

$\begin{equation} G( u_\varepsilon) = \beta^2 \partial_x u_\varepsilon-\delta \partial_{x}^2 u_\varepsilon- \varepsilon \partial_{x}^3 u_\varepsilon. \end{equation}$

(3.5)

Since $0 < \varepsilon < 1$ , thanks to Eq (2.5), we have that

$\begin{equation} \begin{split} &{\beta^2\left\| { \partial_x u_\varepsilon} \right\|^2_{L^2((0,T)\times \mathbb{R})}},\, \delta^2\left\| { \partial_{x}^2 u_\varepsilon} \right\|^2_{L^2((0,T)\times \mathbb{R})}\le C(T),\\ & \varepsilon^2\left\| { \partial_{x}^3 u_\varepsilon} \right\|^2_{L^2((0,T)\times \mathbb{R})}\le C(T). \end{split} \end{equation}$

(3.6)

Therefore, by Eqs (3.5) and (3.6), we have that

$\begin{equation} \{ \partial_x\left(G( u_\varepsilon)\right)\}_{ \varepsilon > 0} \quad {\rm{\ is\ bounded\ in}}\ { L^2(0, T ; H^{-1}( \mathbb{R})) }. \end{equation}$

(3.7)

We claim that

$\begin{equation} \displaystyle {\int}_{0}^{T}\!\!\!\displaystyle {\int}_{ \mathbb{R}}(f'( u_\varepsilon))^2( \partial_x u_\varepsilon)^2 dtdx\le C(T). \end{equation}$

(3.8)

Thanks to Eqs (2.4) and (2.5),

$\begin{equation*} \displaystyle {\int}_{0}^{T}\!\!\!\displaystyle {\int}_{ \mathbb{R}}(f'( u_\varepsilon))^2( \partial_x u_\varepsilon)^2 dtdx\le \left\| {f'} \right\|^2_{L^{\infty}(-C(T),C(T))}\displaystyle {\int}_{0}^{T}\left\| { \partial_x u_\varepsilon(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}dt\le C(T). \end{equation*}$

Moreover, thanks to Eq (2.3),

$\begin{equation} \vert\kappa\vert\displaystyle {\int}_{0}^{T}\!\!\!\displaystyle {\int}_{ \mathbb{R}}( u_\varepsilon)^2 dx \le C(T). \end{equation}$

(3.9)

We have that

$\begin{equation} \gamma^2\displaystyle {\int}_{0}^T\!\!\!\displaystyle {\int}_{ \mathbb{R}}(\vert u_\varepsilon\vert u_\varepsilon)^2 dsdx\le C(T). \end{equation}$

(3.10)

In fact, thanks to Eqs (2.3) and (2.4),

$\begin{align*} \gamma^2\displaystyle {\int}_{0}^T\!\!\!\displaystyle {\int}_{ \mathbb{R}}(\vert u_\varepsilon\vert u_\varepsilon)^2 dsdx\le& \gamma^2\left\| { u_\varepsilon} \right\|^2_{L^{\infty}((0,T)\times \mathbb{R})}\displaystyle {\int}_{0}^T\!\!\!\displaystyle {\int}_{ \mathbb{R}}( u_\varepsilon)^2 dsdx\\ \le&C(T)\displaystyle {\int}_{0}^T\!\!\!\displaystyle {\int}_{ \mathbb{R}}( u_\varepsilon)^2 dsdx\le C(T). \end{align*}$

Therefore, Eq (3.4) follows from Eqs (3.7)–(3.10).

Thanks to the Aubin-Lions Lemma, Eqs (3.1) and (3.2) hold.

Consequently, arguing as in [, Theorem 1.1], $u$ is solution of Eq (1.1) and, thanks to Lemmas 2.1–2.3 and Eqs (2.4), (1.8) holds. □

Proof of Theorem 1.1. Lemma 3.1 gives the existence of a solution of Eq (1.1).

We prove Eq (1.9). Let $u_1$ and $u_2$ be two solutions of Eq (1.1), which verify Eq (1.8), that is,

$\begin{align*} &\begin{cases} \partial_t u_i+ \partial_x f(u_i)-\beta^2 \partial_{x}^2 u_i+\delta \partial_{x}^3 u_i +\kappa u_i +\gamma^2\vert u_i\vert u_i = 0,&\quad 0 < t < T, \quad x\in \mathbb{R},\\ u_i(0,x) = u_{i,\,0}(x), &\quad x\in \mathbb{R}, \end{cases}\quad i = 1,2. \end{align*}$

Then, the function

$\begin{equation} \omega(t,x) = u_1(t,x)-u_2(t,x), \end{equation}$

(3.11)

is the solution of the following Cauchy problem:

$\begin{equation} \begin{cases} \partial_t\omega+ \partial_x\left( f(u_1)-f(u_2)\right)-\beta^2 \partial_{x}^2\omega+\delta \partial_{x}^2\omega \\ \qquad\qquad+\kappa\omega+\gamma^2\left(\vert u_1\vert u_1-\vert u_2\vert u_2\right) = 0, &\quad 0 < t < T, \quad x\in \mathbb{R},\\ \omega(0,x) = u_{1,\,0}(x)-u_{2,\,0}(x), &\quad x\in \mathbb{R}. \end{cases} \end{equation}$

(3.12)

Fixed $T > 0$ , since $u_1, \, u_2\in H^1(\mathbb{R})$ , for every $0\le t\le T$ , we have that

$\begin{equation} \left\| {u_1} \right\|_{L^{\infty}((0,T)\times \mathbb{R})},\, \left\| {u_2} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\le C(T). \end{equation}$

(3.13)

We define

$\begin{equation} g = \frac{f(u_1)-f(u_2)}{\omega} \end{equation}$

(3.14)

and observe that, by Eq (3.13), we have that

$\begin{equation} \vert g\vert\le \left\| {f'} \right\|_{L^{\infty}(-C(T),\,C(T))}\le C(T). \end{equation}$

(3.15)

Moreover, by Eq (3.11) we have that

$\begin{equation} \left\vert \vert u_1\vert-\vert u_2\vert\right\vert\le \vert u_1-u_2\vert = \vert\omega\vert. \end{equation}$

(3.16)

Observe that thanks to Eq (3.11),

$\begin{equation} \begin{split} \vert u_1\vert u_1-\vert u_2\vert u_2 = &\vert u_1\vert u_1-\vert u_1\vert u_2 +\vert u_1\vert u_2 - \vert u_2\vert u_2\\ = &\vert u_1\vert\omega +u_2\left(\vert u_1\vert- \vert u_2\vert\right). \end{split} \end{equation}$

(3.17)

Thanks to Eqs (3.14) and (3.17), Equation (3.12) is equivalent to the following one:

$\begin{equation} \partial_t\omega+ \partial_x ({g}\omega) -\beta^2 \partial_{x}^2\omega+\delta \partial_{x}^3\omega+\kappa\omega+\gamma^2\vert u_1\vert\omega+\gamma^2 u_2\left(\vert u_1\vert- \vert u_2\vert\right) = 0. \end{equation}$

(3.18)

Multiplying Eq (3.18) by $2\omega$ , an integration on $\mathbb{R}$ gives

$\begin{align*} \frac{dt}{dt}&\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} = 2\displaystyle {\int}_{ \mathbb{R}}\omega \partial_t\omega\\ = &-2\displaystyle {\int}_{ \mathbb{R}}\omega \partial_x ({g}\omega)dx+2\beta^2\displaystyle {\int}_{ \mathbb{R}}\omega \partial_{x}^2\omega dx-2\delta\displaystyle {\int}_{ \mathbb{R}}\omega \partial_{x}^3\omega dx \\ &-2\kappa\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_1\vert\omega^2 dx -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}u_2\left(\vert u_1\vert- \vert u_2\vert\right)\omega dx\\ & = 2\displaystyle {\int}_{ \mathbb{R}}{g}\omega \partial_x\omega dx -2\beta^2\left\| { \partial_x\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\delta\displaystyle {\int}_{ \mathbb{R}} \partial_x\omega \partial_{x}^2\omega dx\\ &-2\kappa\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_1\vert\omega^2 dx -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}u_2\left(\vert u_1\vert- \vert u_2\vert\right)\omega dx\\ = & 2\displaystyle {\int}_{ \mathbb{R}}{g}\omega \partial_x\omega dx -2\beta^2\left\| { \partial_x\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}\\ & -2\kappa\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}-2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_1\vert\omega^2 dx -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}u_2\left(\vert u_1\vert- \vert u_2\vert\right)\omega dx. \end{align*}$

Therefore, we have that

$\begin{equation} \begin{split} \left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}&+2\beta^2\left\| { \partial_x\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_1\vert\omega^2 dx\\ = & 2\displaystyle {\int}_{ \mathbb{R}}{g}\omega \partial_x\omega dx -\kappa\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} -2\gamma^2\displaystyle {\int}_{ \mathbb{R}}u_2\left(\vert u_1\vert- \vert u_2\vert\right)\omega dx. \end{split} \end{equation}$

(3.19)

Due to Eqs (3.13), (3.15) and (3.16) and the Young inequality,

$\begin{align*} &2\displaystyle {\int}_{ \mathbb{R}}\vert {g}\vert\vert\omega\vert\vert \partial_x\omega\vert dx\le 2C(T)\displaystyle {\int}_{ \mathbb{R}}\vert\omega\vert\vert \partial_x\omega\vert dx\\ &\qquad = 2\displaystyle {\int}_{ \mathbb{R}}\left\vert\frac{C(T)\omega}{\beta}\right\vert\left\vert\beta \partial_x\omega\right\vert dx\le C(T)\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})} +\beta^2\left\| { \partial_x\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})},\\ &2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_2\vert\left\vert\left(\vert u_1\vert- \vert u_2\vert\right)\right\vert\vert\omega\vert dx\le 2\gamma^2\left\| {u_2} \right\|_{L^{\infty}((0,T)\times \mathbb{R})}\displaystyle {\int}_{ \mathbb{R}}\left\vert\left(\vert u_1\vert- \vert u_2\vert\right)\right\vert\vert\omega\vert dx\\ &\qquad\le C(T)\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{align*}$

It follows from Eq (3.19) that

$\begin{equation*} \left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+\beta^2\left\| { \partial_x\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}+2\gamma^2\displaystyle {\int}_{ \mathbb{R}}\vert u_1\vert\omega^2 dx\le C(T)\left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}. \end{equation*}$

The Gronwall Lemma and Eq (3.12) give

$\begin{equation} \begin{split} \left\| {\omega(t,\cdot)} \right\|^2_{L^2( \mathbb{R})}&+\beta^2 e^{C(T)t}\displaystyle {\int}_{0}^{t}e^{-C(T)s}\left\| { \partial_x\omega(s,\cdot)} \right\|^2_{L^2( \mathbb{R})}ds\\ &+2\gamma^2 e^{C(T)t}\displaystyle {\int}_{0}^{t}\!\!\!\displaystyle {\int}_{ \mathbb{R}}e^{-C(T)s}\vert u_1\vert\omega^2 dsdx\le e^{C(T)t}\left\| {\omega_0} \right\|^2_{L^2( \mathbb{R})}. \end{split} \end{equation}$

(3.20)

Equation (1.9) follows from Eqs (3.11) and (3.20). □

Author contributions

Giuseppe Maria Coclite and Lorenzo Di Ruvo equally contributed to the methodologies, typesetting, and the development of the paper.

Use of AI tools declaration

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

Acknowledgments

Giuseppe Maria Coclite is an editorial boardmember for [Networks and Heterogeneous Media] and was not involved inthe editorial review or the decision to publish this article.

GMC is member of the Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM). GMC has been partially supported by the Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 -Call for tender No. 3138 of 16/12/2021 of Italian Ministry of University and Research funded by the European Union -NextGenerationEUoAward Number: CN000023, Concession Decree No. 1033 of 17/06/2022 adopted by the Italian Ministry of University and Research, CUP: D93C22000410001, Centro Nazionale per la Mobilità Sostenibile, the Italian Ministry of Education, University and Research under the Programme Department of Excellence Legge 232/2016 (Grant No. CUP - D93C23000100001), and the Research Project of National Relevance "Evolution problems involving interacting scales" granted by the Italian Ministry of Education, University and Research (MIUR Prin 2022, project code 2022M9BKBC, Grant No. CUP D53D23005880006). GMC expresses its gratitude to the HIAS - Hamburg Institute for Advanced Study for their warm hospitality.

Conflict of interest

The authors declare there is no conflict of interest.

References

[1]	K. Bollacker, C. Evans, P. Paritosh, T. Sturge, J. Taylor, Freebase: a collaboratively created graph database for structuring human knowledge, in 2008 ACM SIGMOD International Conference on Management of Data (SIGKDD), (2008), 1247–1250. https://doi.org/10.1145/1376616.1376746
[2]	F. M. Suchanek, G. Kasneci, G. Weikum, Yago: a core of semantic knowledge, in 2007 16th International Conference on World Wide Web (WWW), (2007), 697–706. https://doi.org/10.1145/1242572.1242667
[3]	J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, et al., Dbpedia–a large-scale, multilingual knowledge base extracted from Wikipedia, Semantic Web, 6 (2015), 167–195. https://doi.org/10.3233/SW-140134 doi: 10.3233/SW-140134
[4]	M. Wang, X. He, Z. Zhang, L. Liu, L. Qing, Y. Liu, Dual-process system based on mixed semantic fusion for Chinese medical knowledge-based question answering, Math. Biosci. Eng., 20 (2023), 4912–4939. https://doi.org/10.3934/mbe.2023228 doi: 10.3934/mbe.2023228
[5]	Z. Zheng, X. Si, F. Li, E. Y. Chang, X. Zhu, Entity disambiguation with freebase, in 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, (2012), 82–89. https://doi.org/10.1109/WI-IAT.2012.26
[6]	S. Moon, P. Shah, A. Kumar, R. Subba, Opendialkg: Explainable conversational reasoning with attention-based walks over knowledge graphs, in 2019 the 57th Annual Meeting of the Association for Computational Linguistics (ACL), (2019), 845–854. https://doi.org/10.18653/v1/P19-1081
[7]	X. Lu, L. Wang, Z. Jiang, S. Liu, J. Lin, MRE: A translational knowledge graph completion model based on multiple relation embedding, Math. Biosci. Eng., 20 (2023), 5881–5900. https://doi.org/10.3934/mbe.2023253 doi: 10.3934/mbe.2023253
[8]	Q. Wang, Z. Mao, B. Wang, L. Guo, Knowledge graph embedding: A survey of approaches and applications, IEEE Trans. Knowl. Data Eng., 29 (2017), 2724–2743. https://doi.org/10.1109/TKDE.2017.2754499 doi: 10.1109/TKDE.2017.2754499
[9]	J. Xu, X. Qiu, K. Chen, X. Huang, Knowledge graph representation with jointly structural and textual encoding, in 2017 the 26th International Joint Conference on Artificial Intelligence (IJCAI), (2017), 1318–1324. https://doi.org/10.48550/arXiv.1611.08661
[10]	I. Balaˇzevi´c, C. Allen, T. Hospedales, Multi-relational poincar'e graph embeddings, Adv. Neural Inf. Proces. Syst., 32 (2019), 1168–1179. https://doi.org/10.48550/arXiv.1905.09791 doi: 10.48550/arXiv.1905.09791
[11]	S. Vashishth, S. Sanyal, V. Nitin, N. Agrawal, P. Talukdar, Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions, in 2020 the 34th AAAI Conference on Artificial Intelligence (AAAI), (2020), 3009–3016. https://doi.org/10.1609/aaai.v34i03.5694
[12]	H. Mousselly-Sergieh, T. Botschen, I. Gurevych, S. Roth, A multimodal translation-based approach for knowledge graph representation learning, in 2018 the Seventh Joint Conference on Lexical and Computational Semantics, (2018), 225–234. https://doi.org/10.18653/v1/S18-2027
[13]	N. Veira, B. Keng, K. Padmanabhan, A. G. Veneris, Unsupervised embedding enhancements of knowledge graphs using textual associations, in 2019 the 28th International Joint Conference on Artificial Intelligence (IJCAI), (2019), 5218–5225. https://doi.org/10.24963/ijcai.2019/725
[14]	L. Yao, C. Mao, Y. Luo, Kg-bert: Bert for knowledge graph completion, preprint, arXiv: 1909.03193.
[15]	J. Devlin, M. W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, in 2019 the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), (2019), 4171–4186. https://doi.org/10.48550/arXiv.1810.04805
[16]	M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, M. Welling, Modeling relational data with graph convolutional networks, in 2018 European Semantic Web Conference, (2018), 593–607. https://doi.org/10.48550/arXiv.1703.06103
[17]	S. Vashishth, S. Sanyal, V. Nitin, P. Talukdar, Composition-based multi-relational graph convolutional networks, in 2020 the International Conference on Learning Representations (ICLR), (2020), 121–134. https://doi.org/10.48550/arXiv.1911.03082
[18]	A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, O. Yakhnenko, Translating embeddings for modeling multi-relational data, Adv. Neural Inf. Process. Syst., 22 (2013), 2787–2795. https://doi.org/10.5555/2999792.2999923 doi: 10.5555/2999792.2999923
[19]	Y. Lin, Z. Liu, M. Sun, Y. Liu, X. Zhu, Learning entity and relation embeddings for knowledge graph completion, in 2015 AAAI Conference on Artificial Intelligence (AAAI), (2015), 2181–2187. https://doi.org/10.1609/aaai.v29i1.9491
[20]	I. Balazevic, C. Allen, T. Hospedales, Tucker: Tensor factorization for knowledge graph completion. In 2019 the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), (2019), 178–189. https://doi.org/10.18653/v1/D19-1522
[21]	M. Nickel, L. Rosasco, T. Poggio, Holographic embeddings of knowledge graphs, in 2016 the 30th AAAI Conference on Artificial Intelligence (AAAI), (2016), 1955–1961. https://doi.org/10.1609/aaai.v30i1.10314
[22]	W. Zhang, B. Paudel, W. Zhang, A. Bernstein, H. Chen, Interaction embeddings for prediction and explanation in knowledge graphs, in 2019 the 12th ACM International Conference on Web Search and Data Mining (WSDM), (2019), 96–104. https://doi.org/10.1145/3289600.3291014
[23]	Y. LeCun, L. Bottou, Y. Bengio, P. Haffffner, Gradient-based learning applied to document recognition, in Proceedings of the IEEE, (1998), 2278–2324. https://doi.org/10.1109/5.726791
[24]	Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, S. Y. Philip, A comprehensive survey on graph neural networks, IEEE Trans. Neural Networks Learn. Syst., 6 (2021), 97–109. https://doi.org/10.1109/TNNLS.2020.2978386 doi: 10.1109/TNNLS.2020.2978386
[25]	Z. Xie, G. Zhou, J. Liu, X. Huang, Reinceptione: Relation-aware inception network with joint local-global structural information for knowledge graph embedding, in 2020 the 58th Annual Meeting of the Association for Computational Linguistics (ACL), (2020), 5929–5939. https://doi.org/10.18653/v1/2020.acl-main.526
[26]	D. Q. Nguyen, T. D. Nguyen, D. Q. Nguyen, D. Phung, A novel embedding model for knowledge base completion based on convolutional neural network, in 2018 the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), (2018), 327–333. https://doi.org/10.18653/v1/N18-2053
[27]	I. Balaevicx, C. Allen, T. M. Hospedales, Hypernetwork knowledge graph embeddings, in 2019 the 28th International Conference on Artificial Neural Networks, (2019), 553–565. https://doi.org/10.1007/978-3-030-30493-5_52
[28]	S. Vashishth, S. Sanyal, V. Nitin, P. Talukdar, Composition-based multi-relational graph convolutional networks, in 2020 the International Conference on Learning Representations (ICLR), (2020), 321–334. https://doi.org/10.48550/arXiv.1911.03082
[29]	W. Y. Wang, W. W. Cohen, Learning first-order logic embeddings via matrix factorization, in 2016 the 25th International Joint Conference on Artificial Intelligence (IJCAI), (2016), 2132–2138. https://doi.org/10.5555/3060832.3060919
[30]	B. Jagvaral, W. K. Lee, J. S. Roh, M. S. Kim, Y. T. Park, Path-based reasoning approach for knowledge graph completion using cnn-bilstm with attention mechanism, Expert Syst. Appl., 142 (2020), 112960. https://doi.org/10.1016/j.eswa.2019.112960 doi: 10.1016/j.eswa.2019.112960
[31]	R. Socher, D. Chen, C. D. Manning, A. Ng, Reasoning with neural tensor networks for knowledge base completion, Adv. Neural Inf. Process. Syst., 2013 (2013), 926–934. https://doi.org/10.5555/2999611.2999715 doi: 10.5555/2999611.2999715
[32]	X. Gao, Y. Wang, W. Hou, Z. Liu, X. Ma, Multi-view Clustering for integration of gene expression and methylation data with tensor decomposition and self-representation learning, IEEE/ACM Trans. Comput. Biol. Bioinf., 2022 (2022). https://doi.org/10.1109/TCBB.2022.3229678 doi: 10.1109/TCBB.2022.3229678
[33]	D. Li, S. Zhang, X. Ma, Dynamic module detection in temporal attributed networks of cancers, IEEE/ACM Trans. Comput. Biol. Bioinf., 4 (2022), 2219–2230. https://doi.org/10.1109/TCBB.2021.3069441 doi: 10.1109/TCBB.2021.3069441
[34]	X. Ma, W. Zhao, W. Wu, Layer-specific modules detection in cancer multi-layer networks, IEEE/ACM Trans. Comput. Biol. Bioinf., 2022 (2022). https://doi.org/10.1109/TCBB.2022.3176859 doi: 10.1109/TCBB.2022.3176859
[35]	X. Gao, X. Ma, W. Zhang, J. Huang, H. Li, Y. Li, et al., multi-view clustering with self-representation and structural constraint, IEEE Trans. Big Data, 4 (2022), 882–893. https://doi.org/10.1109/TBDATA.2021.3128906 doi: 10.1109/TBDATA.2021.3128906
[36]	R. Xie, Z. Liu, H. Luan, M. Sun, Image-embodied knowledge representation learning, in 2017 the 26th International Joint Conference on Artificial Intelligence (IJCAI), (2017), 3140–3146. https://doi.org/10.24963/ijcai.2017/438
[37]	P. Pezeshkpour, L. Chen, S. Singh, Embedding multimodal relational data for knowledge base completion, in 2018 the Conference on Empirical Methods in Natural Language Processing (EMNLP), (2018), 3208–3218. https://doi.org/10.18653/v1/D18-1359
[38]	J. Yuan, N. Gao, J. Xiang, Transgate: knowledge graph embedding with shared gate structure, in 2019 the AAAI Conference on Artificial Intelligence (AAAI), (2019), 3100–3107. https://doi.org/10.1609/AAAI.V33I01.33013100
[39]	S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031 doi: 10.1109/TPAMI.2016.2577031
[40]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, Adv. Neural Inf. Process. Syst., (2017), 5998–6008. https://doi.org/10.48550/arXiv.1706.03762 doi: 10.48550/arXiv.1706.03762
[41]	Y. Kim, Convolutional neural networks for sentence classification, preprint, arXiv: 1408.5882.
[42]	Z. Yu, J. Yu, J. Fan, D. Tao, Multi-modal factorized bilinear pooling with co-attention learning for visual question answering, in 2017 the IEEE International Conference on Computer Vision (ICCV), (2017), 1821–1830. https://doi.org/10.1109/ICCV.2017.202
[43]	T. Dettmers, M. Pasquale, S. Pontus, S. Riedel, Convolutional 2d knowledge graph embeddings, in 2018 the 32th AAAI Conference on Artificial Intelligence (AAAI), (2018), 1811–1818. https://doi.org/10.1609/aaai.v32i1.11573
[44]	K. Toutanova, D. Chen, Observed versus latent features for knowledge base and text inference, in 2015 the 3rd workshop on continuous vector space models and their compositionality, (2015), 57–66. https://doi.org/10.18653/v1/W15-4007
[45]	D. Kingma, J. Ba, Adam: A method for stochastic optimization, Comput. Sci., 34 (2014), 56–67. https://doi.org/10.48550/arXiv.1412.6980 doi: 10.48550/arXiv.1412.6980
[46]	B. Yang, S. W. Yih, X. He, J. Gao, L. Deng, Embedding entities and relations for learning and inference in knowledge bases, in 2015 International Conference on Learning Representations (ICLR), (2015), 345–358. https://doi.org/10.48550/arXiv.1412.6575
[47]	S. Wang, X. Wei, C. N. Santos, Z. Wang, R. Nallapati, A. Arnold, et al., Mixed-curvature multi-relational graph neural network for knowledge graph completion, in 2021 the International World Wide Web Conference (WWW), (2021), 1761–1771. https://doi.org/10.1145/3442381.3450118
[48]	T. Trouillon, J. Welbl, S. Riedel, xE. Gaussier, G. Bouchard, Complex embeddings for simple link prediction, in 2016 the 33rd International Conference on Machine Learning (ICML), (2016), 2071–2080. https://doi.org/10.48550/arXiv.1606.06357

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Mathematical Biosciences and Engineering

3.9

Metrics

Article views(2513) PDF downloads(146) Cited by(2)

Preview PDF

Download XML

Export Citation

Article outline

Show full outline

Mathematical Biosciences and Engineering

Knowledge graph embedding by fusing multimodal content via cross-modal learning

Related Papers:

Abstract

1. Introduction

2. Vanishing viscosity approximation

3. Proof of Theorem 1.1

Author contributions

Use of AI tools declaration

Acknowledgments

Conflict of interest

References

Reader Comments

通讯作者: 陈斌, bchen63@163.com

Metrics

Figures and Tables

Other Articles By Authors

Catalog

Abstract

1. Introduction

2. Vanishing viscosity approximation

3. Proof of Theorem 1.1

Author contributions

Use of AI tools declaration

Acknowledgments

Conflict of interest

References

Mathematical Biosciences and Engineering

Knowledge graph embedding by fusing multimodal content via cross-modal learning

Related Papers:

Abstract

1. Introduction

2. Vanishing viscosity approximation

3. Proof of Theorem 1.1

Author contributions

Use of AI tools declaration

Acknowledgments

Conflict of interest

References

Reader Comments

通讯作者: 陈斌, bchen63@163.com

Metrics

Figures and Tables

Other Articles By Authors

Related pages

Tools

Export File

Citation

Format

Content

Catalog

Abstract

1. Introduction

2. Vanishing viscosity approximation

3. Proof of Theorem 1.1

Author contributions

Use of AI tools declaration

Acknowledgments

Conflict of interest

References