17 citations · 45 across the 15 of their papers we have counts for
15 papers
Finite-Time Doppler Covariance Spectroscopy in the AdS/CFT correspondence
Feiyi Liu, Shiyang Chen, Yang Wang
From a holographic perspective, we formulate an exact finite-duration covariance framework that resolves chirality in the AdS/CFT correspondence. In a rotating BTZ black hole space…
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…
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…
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…
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…
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…
Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model
Xiangna Chen, Feiyi Liu, Weibing Deng +5
Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the…
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…
Kernel Fusion in Atomistic Spin Dynamics Simulations on Nvidia GPUs using Tensor Core
Hongwei Chen, Shiyang Chen, Joshua J. Turner +1
In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of…
Study of phase transition of Potts model with Domain Adversarial Neural Network
Xiangna Chen, Feiyi Liu, Shiyang Chen +5
A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only nee…
Transfer learning of phase transitions in percolation and directed percolation
Jianmin Shen, Feiyi Liu, Shiyang Chen +6
The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural…
Machine learning of pair-contact process with diffusion
Jianmin Shen, Wei Li, Shengfeng Deng +3
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition.…
Machine learning Hadron Spectral Functions in Lattice QCD
Shi-Yang Chen, Heng-Tong Ding, Fei-Yi Liu +2
Hadron spectral functions carry all the information of hadrons and are encoded in the Euclidean two-point correlation functions. The extraction of hadron spectral functions from th…
Machine learning spectral functions in lattice QCD
S. -Y. Chen, H. -T. Ding, F. -Y. Liu +2
We study the inverse problem of reconstructing spectral functions from Euclidean correlation functions via machine learning. We propose a novel neural network, SVAE, which is based…
Practical Guide of Using Kendall's τ in the Context of Forecasting Critical Transitions
Shiyang Chen, Amin Ghadami, Bogdan I. Epureanu
Recent studies demonstrate that trends in indicators extracted from measured time series can indicate approaching to an impending transition. Kendall's τ coefficient is often used…