10 citations · 14 across the 8 of their papers we have counts for
7 papers · 1 filter
Exact autoregressive sampling of planar Ising spin glasses via the Kac--Ward theory
Jing Liu, Tao Chen, Tianrui Che +3
Exact sampling from the Boltzmann distribution of spin glasses remains an outstanding challenge: Markov chain Monte Carlo methods suffer from critical slowing down and metastable t…
Percolation in the three-dimensional Ising model
Jinhong Zhu, Tao Chen, Zhiyi Li +2
Geometric representations provide a useful perspective on critical phenomena in the Ising model. In a recent study [Phys. Rev. E 112, 034118 (2025)], we found that the two-dimensio…
Tensor Network Markov Chain Monte Carlo: Efficient Sampling of Three-Dimensional Spin Glasses and Beyond
Tao Chen, Jing Liu, Youjin Deng +1
Sampling the three-dimensional (3D) spin glass -- i.e., generating equilibrium configurations of a 3D lattice with quenched random couplings -- is widely regarded as one of the cen…
BatchTNMC: Efficient sampling of two-dimensional spin glasses using tensor network Monte Carlo
Tao Chen, Jingtong Zhang, Jing Liu +2
Efficient sampling of two-dimensional statistical physics systems remains a central challenge in computational statistical physics. Traditional Markov chain Monte Carlo (MCMC) meth…
Percolation in the two-dimensional Ising model
Tao Chen, Jinhong Zhu, Wei Zhong +2
The study of the Ising model from a percolation perspective has played a significant role in the modern theory of critical phenomena. We consider the celebrated square-lattice Isin…
Correction-to-scaling exponent for percolation and the Fortuin--Kasteleyn Potts model in two dimensions
Yihao Xu, Tao Chen, Zongzheng Zhou +2
The number of clusters (per site) of size , a central quantity in percolation theory, displays at criticality an algebraic scaling behavior of the form $n_s\simeq s^{-τ}\,…
Tensor network Monte Carlo simulations for the two-dimensional random-bond Ising model
Tao Chen, Erdong Guo, Wanzhou Zhang +2
Disordered lattice spin systems are crucial in both theoretical and applied physics. However, understanding their properties poses significant challenges for Monte Carlo simulation…