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2021

HMC with Normalizing Flows

Foreman, Sam, Izubuchi, Taku, Jin, Luchang et al.

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We propose using Normalizing Flows as a trainable kernel within the molecular dynamics update of Hamiltonian Monte Carlo (HMC).

By learning (invertible) transformations that simplify our dynamics, we can outperform traditional methods at generating independent configurations.
We show that, using a carefully constructed network architecture, our approach can be easily scaled to large lattice volumes with minimal retraining effort.
The source code for our implementation is publicly available online at https://github.com/nftqcd/fthmc.

HMC with Normalizing Flows · Around

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G. Kanwar, M.S. Albergo, D. Boyda, K. Cranmer, D.C. Hackett, S. Racanière et al., Equivariant flow-based sampling for lattice gauge theory , Phys. Rev. Lett. 125
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M. Lüscher, Trivializing Maps, the Wilson Flow and the HMC Algorithm , Communications in Mathematical Physics 293
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Then

V. Elvira, L. Martino and C.P. Robert, Rethinking the Effective Sample Size , arXiv e-prints (2018) arXiv:1809.04129 [ 1809.04129 ]
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2018
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10.5281/zenodo.2893252
T.A. Caswell, M. Droettboom, J. Hunter, E. Firing, A. Lee, J. Klymak et al., matplotlib/matplotlib v3.1.0 , May, 2019 · 2019
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Later among the works it cites.
M.S. Albergo, D. Boyda, D.C. Hackett, G. Kanwar, K. Cranmer, S. Racanière et al., Introduction to Normalizing Flows for Lattice Field Theory , arXiv e-prints (2021) [ 2101.08176 ]
Original
2021
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