使用对称自动编码器对物理系统进行基准重定
Redatuming physical systems using symmetric autoencoders
摘要：
本文考虑物理系统描述的隐藏状态和间接观察到的重复测量损坏的未建模的滋扰参数。基于网络的表示学习将相干信息（相对于状态）与不相干的干扰信息（相对于感测）分开。代替物理模型，该表示使用对称性和随机正则化来通知称为SymAE的自动编码器架构。它支持重新基准化，即，创建虚拟数据实例，其中干扰在测量中被统一。
英文摘要：
This paper considers physical systems described by hidden states and indirectly observed through repeated measurements corrupted by unmodeled nuisance parameters. A network-based representation learns to disentangle the coherent information (relative to the state) from the incoherent nuisance information (relative to the sensing). Instead of physical models, the representation uses symmetry and stochastic regularization to inform an autoencoder architecture called SymAE. It enables redatuming, i.e., creating virtual data instances where the nuisances are uniformized across measurements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
