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Global Gradient Estimates for Nonlinear Elliptic Equations with Vanishing Neumann Data in a Convex Domain

Published: 01 April 2017 Publication History

Abstract

In this paper, we obtain the following global Lq estimates fpźLq(Ω)źźupźLq(Ω)for anyqź1$$\left|\mathbf{f}\right|^{p } \in L^{q}({\Omega}) \Rightarrow \left|\nabla u\right|^{p } \in L^{q}({\Omega}) \quad \text{for any} ~~q\ge 1 $$ in a convex domain Ω of weak solutions for nonlinear elliptic equations of p-Laplacian type with vanishing Neumann data divAźuźźupź22Aźu=div|f|pź2finΩ,Aźuźźupź22Aźuźź=|f|pź2fźźonźΩ,$$\begin{array}{@{}rcl@{}} \text{div} \left( \left( A \nabla u \cdot \nabla u\right)^{\frac{p -2}{2}} A \nabla u \right) & =& \text{div} \left( | \mathbf{f}|^{p-2} \mathbf{f} \right) \quad\text{in} ~~{\Omega},\\ \left( A \nabla u \cdot \nabla u\right)^{\frac{p -2}{2}} A \nabla u \cdot \mathbf{\nu} &=& | \mathbf{f}|^{p -2} \mathbf{f}\cdot \mathbf{\nu} \quad \quad \text{on}~~ \partial{\Omega}, \end{array} $$ where ź is the outwardpointing unit normal to źΩ. Our argument is based on the works of Banerjee and Lewis (Nonlinear Anal 100:78---85, 2014), Kinnunen and Zhou (Comm Partial Differential Equations 24(11&12):2043---2068, 1999, Differential and Integral Equations 14(4):475---492, 2001), and Byun, Wang, and Zhou (Comm Pure Appl Math 57(10):1283---1310, 2004, J Funct Anal 20(3):617---637, 2007). In the proof of the above result, we only focus on the boundary case while the interior case can be obtained as a corollary.

References

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  1. Global Gradient Estimates for Nonlinear Elliptic Equations with Vanishing Neumann Data in a Convex Domain

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    Published In

    cover image Journal of Dynamical and Control Systems
    Journal of Dynamical and Control Systems  Volume 23, Issue 2
    April 2017
    253 pages

    Publisher

    Kluwer Academic Publishers

    United States

    Publication History

    Published: 01 April 2017

    Author Tags

    1. 35B45
    2. 35J60
    3. Divergence
    4. Elliptic
    5. Global
    6. Gradient
    7. Lq
    8. Nonlinear
    9. p-Laplacian

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