
























Abstract:We study the \emph{generalized Stokes operator} \begin{equation*} \bsXi \ede \bsXi _{V,V_0} \ede \left(\begin{array}{ccc} \bsL + V & \nabla \\ \nabla^* & -V_0 \end{array}\right) \end{equation*} on a \emph{domain with straight cylindrical ends} $\Omega$ using \emph{the method of layer potentials} on $M \supset \Omega$. The operator $\bsXi_{0, 0}$ is the classical Stokes operator. Under suitable positivity assumptions on $V$ and $V_{0}$, we prove that $\bsXi$ is Fredholm. This allows us then to define the single- and double-layer potentials $\bsS$ and $\frac12 + \bsK$. Under further positivity assumptions, we prove that $\bsS$ and $\frac12 + \bsK$ are also Fredholm. Under slightly stronger assumptions on $V$ and $V_{0}$, we prove \emph{the invertibility} of the operators $\bsXi$, $\bsS$, and $\frac12 + \bsK$. The invertibility of these operators leads to \emph{well-posedness results} for the associated (linear) Stokes boundary value problem with Dirichlet boundary conditions on $\Omega$. The proofs of these results required us to develop many related tools. In particular, we develop an ``algebra tool kit'' to deal with \emph{limit and jump relations of layer potentials.} We also develop Green formulas and energy estimates for our generalized Stokes operator $\bsXi$ on manifolds with straight cylindrical ends, which requires a careful geometric study of the related differential operators, such as the deformation operator $\Def$. For completeness, we review suitable classes of pseudodifferential operators on manifolds with straight cylindrical ends that were studied in some previous papers of ours (including ``The Stokes operator on manifolds with cylindrical ends,'' J. Diff. Equations, 2024). As an application, we prove the well-posedness result for the Dirichlet problem for the generalized Navier-Stokes system with small data on a domain with cylindrical ends.
From: Victor Nistor [view email]
[v1]
Mon, 11 May 2026 16:59:02 UTC (211 KB)
[v2]
Thu, 28 May 2026 14:21:22 UTC (215 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。