arXiv Machine Learning
Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory
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arXiv Machine Learning
Jul 9
Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories
arXiv:2607. 07127v1 Announce Type: cross Abstract: Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials.
arXiv Machine Learning
Jul 24
SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
arXiv:2505. 19619v3 Announce Type: replace Abstract: Deep generative models have recently garnered significant attention across various fields, from physics to chemistry, where sampling from unnormalized Boltzmann-like distributions represents a fundamental challenge.
arXiv Machine Learning
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Quenched Ensemble Sampling
arXiv:2609.15894v1 Announce Type: cross Abstract: Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states cha...
arXiv Machine Learning
Jun 4
Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem
arXiv:2605. 26814v2 Announce Type: replace-cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations.
arXiv Machine Learning
Jun 29
Sampling the Schwinger Model with Gauge-Equivariant Diffusion
arXiv:2606. 27481v1 Announce Type: cross Abstract: We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model.
arXiv Machine Learning
Jul 20
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.