Abstract
An inpainting method based on mechanism learning is proposed from the perspective of inverse problems. The underlying data mechanism, characterized by linear differential equations, is identified from data on the known area and then exploited to infer that on the missing part. Special attention is paid to incorporation of historical or prior information as higher order mechanism. Numerical examples show effectiveness, robustness and flexibility of the method and it performs particularly well over mechanism/scientific data.
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