Convergence analysis for forward and inverse problems in singularly perturbed time-dependent reaction-advection-diffusion equations
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In this paper, by employing the asymptotic method, we prove the existence and uniqueness of a smoothing solution for a singularly perturbed Partial Differential Equation (PDE) with a small parameter. As a by-product, we obtain a reduced PDE model with vanished high order derivative terms, which is close to the original PDE model in any order of this small parameter in the whole domain except a negligible transition layer. Based on this reduced forward model, we propose an efficient two step regularization algorithm for solving inverse source problems governed by the original PDE. Convergence rate results are studied for the proposed regularization algorithm, which shows that this simplification will not (asymptotically) decrease the accuracy of the inversion result when the measurement data contains noise. Numerical examples for both forward and inverse problems are given to show the efficiency of the proposed numerical approach.
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