A Heat Kernel Expectation Approach to Boundary-Corrected Li--Yau Estimates for the Dirichlet Heat Equation

Abstract

In this paper, we establish an explicit boundary-corrected Li--Yau type gradient estimate for positive solutions of the Dirichlet heat equation on the Euclidean half-space. The main idea is to exploit the reflection structure of the Dirichlet heat kernel and introduce a normalized kernel-induced probability measure. Under this representation, logarithmic derivatives of the heat kernel become expectations of explicit kernel quantities. The reflected Gaussian component generates a hyperbolic correction term involving \[ \coth\left(\frac{x_n y_n}{2t}\right), \] which has no analogue in the whole Euclidean heat equation. Using Jensen's inequality and the sharp estimate \[ 0 0, \] we prove that every positive solution satisfies \[ \Delta\log w(x,t) \geq -\frac n{2t} -\frac1{x_n^2}. \] The first term represents the classical Euclidean Li--Yau diffusion scaling, while the second term is an explicit inverse-square correction determined by the distance to the Dirichlet boundary. Our approach provides a direct kernel interpretation of the boundary effect and suggests possible extensions to more general domains.