arXiv Machine Learning
Quenched Ensemble Sampling
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arXiv Machine Learning
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Neural Transport Nested Sampling
Neural Transport Nested Sampling (NTNS) is a new sampling algorithm that merges nested sampling with neural flow-based methods to sample from Boltzmann distributions of molecular systems. It employs a flow-matching velocity as the drift in a Metropolis–Hastings corrected Langevin kernel within a nested sampling loop, requiring only target energy evaluations and enabling scalable estimation of the full partition function for high-dimensional particle systems. Benchmarks on Lennard–Jones clusters up to 55 particles show NTNS reduces interatomic distance and energy Wasserstein errors by more than an order of magnitude compared to leading neural baselines, while also providing a calibrated, temperature-resolved partition function estimate that captures phase structure from a single run.
arXiv Machine Learning
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Leveraging generative models to assist Monte Carlo sampling
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
arXiv Statistics ML
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Overcoming critical slowing down in frustrated spin systems by learned multiscale sampling
arXiv:2608.31114v1 Announce Type: cross Abstract: Cluster algorithms, such as the Swendsen--Wang and Wolff methods, are among the most successful MCMC methods for mitigating critical slowing down in...
arXiv Machine Learning
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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
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Interacting particle guidance for sampling reward-tilted generative priors
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arXiv Machine Learning
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Diffusion-warm sampling of the XY model enables fast thermalization at scale
arXiv:2606. 30773v1 Announce Type: cross Abstract: We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models.