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Found 2 papers, 1 papers with code
Towards probabilistic Weather Forecasting with Conditioned Spatio-Temporal Normalizing Flows
Generative normalizing flows are able to model multimodal spatial distributions, and they have been shown to model temporal correlations successfully as well.
Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z) through an invertible neural network under the change of variables formula.
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