2021/08/05 by Pawan Bharadwaj, Bharadwaj, Pawan, Matthew Li +3 · 1 citation
Computer Science · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Geophysics (physics.geo-ph) #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2108.02537
openalex publication_date 2021/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.