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Found 4 papers, 3 papers with code
Deep generative modelling of canonical ensemble with differentiable thermal properties
Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range.
Learning Non-linear Wavelet Transformation via Normalizing Flow
Wavelet transformation stands as a cornerstone in modern data analysis and signal processing.
Neural Canonical Transformation with Symplectic Flows
We construct flexible and powerful canonical transformations as generative models using symplectic neural networks.
Neural Network Renormalization Group
We present a variational renormalization group (RG) approach using a deep generative model based on normalizing flows.
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