Time-dependent Robin heat equation via Markovian switching

TL;DR

This paper studies a Robin heat equation with a Markovian switching boundary parameter, analyzing both averaged and fixed switching scenarios, and applies results to biophysical receptor models.

Contribution

It introduces a stochastic framework for Robin boundary conditions with Markovian switching and derives solution representations and averaging principles.

Findings

Solution via semigroup and Feynman-Kac formula in annealed case

Characterization through non-autonomous evolution family in quenched case

Convergence to deterministic Robin problem in fast-switching limit

Abstract

This paper investigates the heat equation on a bounded domain with a Robin boundary condition, where the reactivity parameter (or killing rate) is modeled as a continuous-time Markov chain. We analyze the system under two stochastic frameworks using a functional analytic approach. First, we examine the annealed case, which accounts for the joint stochasticity of the diffusion and the switching mechanism. We describe the solution via a strongly continuous contraction semigroup on a product space. We identify its infinitesimal generator, which incorporates the state-dependent Robin conditions into its domain, and provide a corresponding Feynman-Kac formula. Second, we study the quenched setting for fixed realizations of the switching paths. We characterize the solution through a non-autonomous evolution family (propagator) and derive a Feynman-Kac-type representation involving the boundary local time of a reflected Brownian motion. We prove an averaging principle in the fast-switching limit, showing that the system converges to a deterministic Robin problem. These results are applied to a biophysical model of stochastically gated receptors on cell membranes.