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Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
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abstract
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architectures used so far for this task, our proposal is based on a shallow design and incorporates the symmetries of the problem. We test our model on the $\phi^4$ theory, showing that it systematically outperforms a realNVP baseline in sampling efficiency, with the difference between the two increasing for larger lattices. On the largest lattice we consider, of size $32\times 32$, we improve a key metric, the effective sample size, from 1% to 66% w.r.t. the realNVP baseline.
Forward citations
Cited by 2 Pith papers
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Exploring Generative Networks for Manifolds with Non-Trivial Topology
A GFlowNet-inspired diffusion sampler is proposed and shown, on toy and 2D lattice scalar models, to generate configurations across disconnected sectors that normalizing flows and plain diffusion models miss.
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Machine-learning approaches to accelerating lattice simulations
A review of unbiased machine-learning acceleration methods for lattice field theory, covering flow-based sampling, contour deformations, control variates, and surrogate observables.
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