Principal component analysis of absorbing state phase transitions

被引：0
作者：
Muzzi, Cristiano [1 ,2 ]
Cortes, Ronald Santiago [1 ,3 ]
Bhakuni, Devendra Singh [3 ]
Jelic, Asja [3 ]
Gambassi, Andrea [1 ,2 ]
Dalmonte, Marcello [3 ]
Verdel, Roberto [3 ]
机构：
[1] SISSA Int Sch Adv Studies, Via Bonomea 265, I-34136 Trieste, Italy
[2] Ist Nazl Fis Nucl, Sez Trieste, via Valerio 2, I-34127 Trieste, Italy
[3] ICTP Abdus Salam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy
来源：
PHYSICAL REVIEW E | 2024年 / 110卷 / 06期
关键词：
RENORMALIZATION-GROUP; UNIVERSALITY CLASS; CRITICAL EXPONENTS; PERCOLATION; BEHAVIOR; SYSTEMS; MODELS;
D O I：
10.1103/PhysRevE.110.064121
中图分类号：
O35 [流体力学]; O53 [等离子体物理学];
学科分类号：
070204 ; 080103 ; 080704 ;
摘要：
We perform a principal component analysis (PCA) of two one-dimensional lattice models belonging to distinct nonequilibrium universality classes-directed bond percolation and branching and annihilating random walks with an even number of offspring. We find that the uncentered PCA of data sets storing various system's configurations can be successfully used to determine the critical properties of these nonequilibrium phase transitions. In particular, in both cases, we obtain good estimates of the critical point and the dynamical critical exponent of the models. For directed bond percolation, we are furthermore able to extract critical exponents associated with the correlation length and the order parameter. We discuss the relation of our analysis with low-rank approximations of data sets.
引用
收藏
页数：17
相关论文
共 86 条
  • [21] Martiniani S., Chaikin P. M., Levine D., Quantifying hidden order out of equilibrium, Phys. Rev. X, 9, (2019)
  • [22] Seif A., Hafezi M., Jarzynski C., Machine learning the thermodynamic arrow of time, Nat. Phys, 17, (2021)
  • [23] Gillman E., Rose D. C., Garrahan J. P., Combining reinforcement learning and tensor networks, with an application to dynamical large deviations, Phys. Rev. Lett, 132, (2024)
  • [24] Zhao H., Storey B. D., Braatz R. D., Bazant M. Z., Learning the physics of pattern formation from images, Phys. Rev. Lett, 124, (2020)
  • [25] Nicolaou Z. G., Huo G., Chen Y., Brunton S. L., Kutz J. N., Data-driven discovery and extrapolation of parameterized pattern-forming dynamics, Phys. Rev. Res, 5, (2023)
  • [26] Tang Y., Liu J., Zhang J., Zhang P., Learning nonequilibrium statistical mechanics and dynamical phase transitions, Nat. Commun, 15, (2024)
  • [27] Shen J., Li W., Deng S., Xu D., Chen S., Liu F., Machine learning of pair-contact process with diffusion, Sci. Rep, 12, (2022)
  • [28] Venderley J., Khemani V., Kim E.-A., Machine learning out-of-equilibrium phases of matter, Phys. Rev. Lett, 120, (2018)
  • [29] Bohrdt A., Kim S., Lukin A., Rispoli M., Schittko R., Knap M., Greiner M., Leonard J., Analyzing nonequilibrium quantum states through snapshots with artificial neural networks, Phys. Rev. Lett, 127, (2021)
  • [30] Shen J., Liu F., Chen S., Xu D., Chen X., Deng S., Li W., Papp G., Yang C., Transfer learning of phase transitions in percolation and directed percolation, Phys. Rev. E, 105, (2022)
123456789