5 papers
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
Moxian Qian
Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correctio…
Stochastic Path Sampler For Lattice Field Theory
Shiyang Chen, Moxian Qian, Gert Aarts +2
In lattice field theory, target distributions are known only up to normalization, (\tildeÏ(Ï)\propto e^{-S(Ï)}), while the partition function is intractable. Markov chain Monte…
Operator Spectroscopy of Trained Lattice Samplers
Moxian Qian
Trained lattice samplers are usually judged by the ensembles they generate. Here we instead analyze the trained field-space function itself: a flow-matching velocity, a diffusion s…
Variational Autoregressive Networks Applied to Field Theory Systems
Moxian Qian, Shiyang Chen
We combine reinforcement learning with variational autoregressive networks (VANs) to perform data-free training and sampling for the discrete Ising model and the continuous …
A Review of Machine Learning for Cavitation Intensity Recognition in Complex Industrial Systems
Yu Sha, Ningtao Liu, Haofeng Liu +10
Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operatio…