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Bayesian tomography using polynomial chaos expansion and deep generative networks
By sampling a low-dimensional prior probability distribution defined in the low-dimensional latent space of such a model, it becomes possible to efficiently sample the physical domain at the price of a generator that is typically highly non-linear.
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