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Found 3 papers, 0 papers with code
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty
The cWGAN developed in this work differs from earlier versions in that its critic is required to be 1-Lipschitz with respect to both the inferred and the measurement vectors and not just the former.
Uncertainty quantification for ptychography using normalizing flows
Ptychography, as an essential tool for high-resolution and nondestructive material characterization, presents a challenging large-scale nonlinear and non-convex inverse problem; however, its intrinsic photon statistics create clear opportunities for statistical-based deep learning approaches to tackle these challenges, which has been underexplored.
Simultaneous Reconstruction and Uncertainty Quantification for Tomography
Tomographic reconstruction, despite its revolutionary impact on a wide range of applications, suffers from its ill-posed nature in that there is no unique solution because of limited and noisy measurements.
