6 papers
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
Moxian Qian
Learned dynamical proposals can generate configurations without providing a tractable endpoint density. Nonequilibrium path probabilities offer a way to correct such proposals, but…
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, (\tildeπ(ϕ)\propto e^{-S(ϕ)}), while the partition function is intractable. Markov chain Monte Car…
Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory
Moxian Qian
Neural generative samplers for lattice field theory can be costly to train and evaluate. When they miss modes or assign them incorrect relative weights, biased observables do not r…
Sequential Autoregressive Block Metropolis--Hastings for Two-Dimensional Lattice Fields
Moxian Qian, Shiyang Chen
We construct a Metropolis-corrected sequential autoregressive block update for continuous lattice fields. A variational autoregressive network (VAN) regenerates the sites of an ord…
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…
Gravitation induced shrinkage of Mercury's orbit
Qian Moxian, Li Xi-Bin, Cao Yongjun
In general relativity, the Mercury's orbit becomes approximately elliptical and the its perihelion has thus an additional advance. We demonstrate, meanwhile, that in comparison of…