VaFES constructs a latent space from reversible collective variables and variationally optimizes a tractable-density generative model to produce a continuous free energy surface from which rare events are directly sampled.
Equivariant flows: sampling configurations for multi- body systems with symmetric energies.arXiv preprint arXiv:1910.00753
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Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
PAINET proposes an SE(3)-equivariant transformer with physics-inspired attention from energy minimization for 3D dynamics modeling, reporting 4.7-41.5% error reductions on human motion, molecular, and protein benchmarks.
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Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
VaFES constructs a latent space from reversible collective variables and variationally optimizes a tractable-density generative model to produce a continuous free energy surface from which rare events are directly sampled.
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Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
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PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling
PAINET proposes an SE(3)-equivariant transformer with physics-inspired attention from energy minimization for 3D dynamics modeling, reporting 4.7-41.5% error reductions on human motion, molecular, and protein benchmarks.