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Redatuming physical systems using symmetric autoencoders
This paper considers physical systems described by hidden states and indirectly observed through repeated measurements corrupted by unmodeled nuisance parameters.
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime
We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales.
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data.
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