A Bayesian thinning algorithm for the point source identification of heat equation

Abstract: In this work, we propose a Bayesian thinning algorithm for recovering weighted point source functions in the heat equation from boundary flux observations. The major challenge in the classical Bayesian framework lies in constructing suitable priors for such highly structured unknowns. To address this, we introduce a level set representation on a discretized mesh for the unknown, which enables the infinite-dimensional Bayesian framework to the reconstruction. From another perspective, the point source configuration can be modeled as a marked Poisson point process (PPP), then a thinning mechanism is employed to selectively retain points. These two proposals are complementary with the Bayesian level set sampling generating candidate point sources and the thinning process acting as a filter to refine them. This combined framework is validated through numerical experiments, which demonstrate its accuracy in reconstructing point sources.