A null controllability data assimilation for the bulk-surface heat equation with dynamic boundary conditions
We address the inverse problem of reconstructing the state at a final time $T_0$ of a parabolic equation with dynamic boundary conditions of Wentzell type, from a distributed interior observation on a subdomain $ω\subset Ω$. Our approach combines (i) a Carleman-type observability inequality for the bulk-surface system, with (ii) a Tikhonov-regularized optimal-control reformulation of the data assimilation problem, and (iii) a fully implementable spatio-temporal discretization based on a Lie splitting that decouples the bulk and surface dynamics. We prove an exact reconstruction theorem for the continuous problem, derive a stability inequality, and analyze a penalized discrete scheme. We then propose an adaptive post-processing step that optimally combines the regularized reconstruction with the observation itself; under noisy data, the resulting estimator automatically selects between full post-processing and the raw reconstruction without prior knowledge of the noise level. Extensive numerical experiments on a two-dimensional Wentzell heat equation validate the method. A semilinear extension to the Allen-Cahn nonlinearity is treated via a Picard outer loop with Schauder fixed-point convergence; two complementary experiments reveal an intrinsic observability-nonlinearity trade-off in the bulk-surface setting.