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To reduce the computational burden at test time, we propose to amortize the computational cost with offline training. After training, we aim to efficiently generate probabilistic FWI solutions with uncertainty. This aim is achieved by exploiting the ability of networks (i.e Normalizing Flows) to learn distributions, such as the Bayesian posterior. The posterior uncertainty is used during training to optimize the receiver sampling.}, keywords = {bayesian, experimental design, FWI, Normalizing flows, SEG, Uncertainty quantification}, url = {https://slimgroup.github.io/IMAGE2023/BayesianFWI/abstract.html}, presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/orozco2023IMAGEabf}, author = {Rafael Orozco and Mathias Louboutin and Felix J. 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