Author(s)

F. Delvare, A. Cimetiere & F. Pons

Abstract

The paper deals with the recovering of lacking data on some part of a domain boundary, from the knowledge of overprescribed data on some other part of the boundary. We introduce two iterative algorithms. Each algorithm reads as a least square fitting of the given data, with a regularization term that fades as iterations go on. Displayed numerical results compare the two algorithms and highlight their accuracy, as well as their robustness. 1 Introduction Given a physical model, an inverse problem consists in determining unmeasurable quantities from measurable quantities. Inverse problems are usually ill-posed in Hadamard's sense because they are very sensitive to errors on measured data. They occur in many scientific fields (Bui [1]). Inverse problems can b

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