We study the effect of using weaker forms of data-fidelity terms in generalized Tikhonov regularization accounting for model uncertainties. We show that relaxed data-consistency conditions can be beneficial for integrating available prior knowledge.

翻译：我们研究了在广义Tikhonov正则化中采用较弱形式的数据保真项以应对模型不确定性时的效应。研究表明，放宽数据一致性条件有助于整合可利用的先验知识。