A VAE framework decomposes KL divergence into index-code mutual information, total correlation, and dimension-wise KL to achieve disentangled and interpretable manifold learning on unsteady fluid flows.
IEEE Transactions on Pattern Analysis and Machine Intelligence44(7), 3614–3633 (2022) 30
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Information decomposition for disentangled and interpretable manifold learning of fluid flows via variational autoencoders
A VAE framework decomposes KL divergence into index-code mutual information, total correlation, and dimension-wise KL to achieve disentangled and interpretable manifold learning on unsteady fluid flows.