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Equivariant Flows: sampling configurations for multi-body systems with symmetric energies
[article]
2019
arXiv
pre-print
Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest. Boltzmann Generators (BG) combine flows and statistical mechanics to sample equilibrium states of strongly interacting many-body systems such as proteins with 1000 atoms. In order to scale and generalize these results, it is essential that the natural symmetries of the probability density - in physics defined by the invariances
arXiv:1910.00753v1
fatcat:4tylnzbyl5dhbeshha5mxr7ade