6 papers
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…
Stochastic first-passage modeling of single-event burnout in SiC power MOSFETs
Feiyi Liu, Min Guo, Shiyang Chen +3
Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stoc…
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 …
Learning phase transitions by siamese neural network
Jianmin Shen, Shiyang Chen, Feiyi Liu +2
The wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised l…
Exploring percolation phase transition in the three-dimensional Ising model with machine learning
Ranran Guo, Xiaobing Li, Rui Wang +3
The percolation study offers valuable insights into the characteristics of phase transition, shedding light on the underlying mechanisms that govern the formation of global connect…
Exploring Generative Networks for Manifolds with Non-Trivial Topology
Shiyang Chen, Gert Aarts, Biagio Lucini
The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations…