Research article

Neumann gradient estimate for nonlinear heat equation under integral Ricci curvature bounds

  • Received: 05 October 2023 Revised: 24 December 2023 Accepted: 01 January 2024 Published: 11 January 2024
  • MSC : Primary 53C44; Secondary 53C55

  • In this paper, we consider a Li-Yau gradient estimate on the positive solution to the following nonlinear parabolic equation

    $ \frac{\partial}{\partial t}f = \Delta f+af(\ln f)^{p} $

    with Neumann boundary conditions on a compact Riemannian manifold satisfying the integral Ricci curvature assumption, where $ p\geq 0 $ is a real constant. This contrasts Olivé's gradient estimate, which works mainly for the heat equation rather than nonlinear parabolic equations and the result can be regarded as a generalization of the Li-Yau [P. Li, S. T. Yau, On the parabolic kernel of the Schrödinger operator, Acta Math., 156 (1986), 153–201] and Olivé [X. R. Olivé, Neumann Li-Yau gradient estimate under integral Ricci curvature bounds, Proc. Amer. Math. Soc., 147 (2019), 411–426] gradient estimates.

    Citation: Hao-Yue Liu, Wei Zhang. Neumann gradient estimate for nonlinear heat equation under integral Ricci curvature bounds[J]. AIMS Mathematics, 2024, 9(2): 3881-3894. doi: 10.3934/math.2024191

    Related Papers:

  • In this paper, we consider a Li-Yau gradient estimate on the positive solution to the following nonlinear parabolic equation

    $ \frac{\partial}{\partial t}f = \Delta f+af(\ln f)^{p} $

    with Neumann boundary conditions on a compact Riemannian manifold satisfying the integral Ricci curvature assumption, where $ p\geq 0 $ is a real constant. This contrasts Olivé's gradient estimate, which works mainly for the heat equation rather than nonlinear parabolic equations and the result can be regarded as a generalization of the Li-Yau [P. Li, S. T. Yau, On the parabolic kernel of the Schrödinger operator, Acta Math., 156 (1986), 153–201] and Olivé [X. R. Olivé, Neumann Li-Yau gradient estimate under integral Ricci curvature bounds, Proc. Amer. Math. Soc., 147 (2019), 411–426] gradient estimates.



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