Skip to content
DraconiticResearch
Search Sources API
201,642 studies31 sourcesUpdated 17 min ago
PreprintCC BY 4.0arXiv

Learning the Emergent Bulk Geometry of the Three Dimensional Ising Model

Abstract

We use Monte Carlo data for the three-dimensional Ising model at the critical temperature to predict an emergent radial geometry of the bulk using machine learning. Specifically, we employ a differentiable solver for a probe field on an asymptotically AdS4 background, trained to reproduce the measured momentum-space two-point functions of the boundary operators (σ and ε). We find that a single metric cannot simultaneously serve both operators. Fitting each channel separately improves its own residual by a factor of three to five, but degrades the other by up to two orders of magnitude. We quantify this tension between the two operators and show that it survives changes to the momentum window and the lattice size. Our results numerically demonstrate that the bulk geometry of a theory with an 𝒪(1) central charge, such as the 3D Ising model, is not captured by a single classical metric.

Related

  1. Emergent one-dimensional Bloch oscillations in coupled Ising layers
    E. S. Ma, Z. Song
    arXivPreprintarXiv licenceFull textCondensed matterarXiv
  2. Thermodynamic geometry of three coupled Ising spins: exact curvature, frustration and bond anisotropy
    Zahra Ebadi, Nayyere Einali Saghavaz, Omid Yahyayi Monem +1
    arXivPreprintCC BY 4.0Full textMathematical physicsStatistical & nonlinear physicsarXiv
  3. Emergent dynamical scaling in the inviscid limit of three-dimensional stochastic Navier–Stokes equation with thermal noise
    Liubov Gosteva, Marc Etienne Brachet, Léonie Canet
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering SciencesPeer-reviewedCC BY 4.0Full textCondensed matterFluid dynamicsHAL
  4. Full Three-dimensional Cell Geometry Matters for Epithelial Fluidization
    Toshikaze Chiba, Jan Rozman, Matej Krajnc
    arXivPreprintCC0Full textBiological & medical physicsarXiv
  5. Phoretic flow in a three-dimensional wedge geometry
    Abdallah Daddi-Moussa-Ider, Semyon Yakubovich, Maciej Lisicki
    arXivPreprintCC BY 4.0Full textCondensed matterFluid dynamicsarXiv
BibTeX
@misc{basu2026learning,
  title = {{Learning the Emergent Bulk Geometry of the Three Dimensional Ising Model}},
  author = {Ritam Basu},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.20210},
  eprint = {2609.20210},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.20210},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Learning the Emergent Bulk Geometry of the Three Dimensional Ising Model
AU  - Ritam Basu
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.20210
UR  - https://arxiv.org/abs/2609.20210
AB  - We use Monte Carlo data for the three-dimensional Ising model at the critical temperature to predict an emergent radial geometry of the bulk using machine learning. Specifically, we employ a differentiable solver for a probe field on an asymptotically AdS_4 background, trained to reproduce the measured momentum-space two-point functions of the boundary operators (σ and ε). We find that a single metric cannot simultaneously serve both operators. Fitting each channel separately improves its own residual by a factor of three to five, but degrades the other by up to two orders of magnitude. We quantify this tension between the two operators and show that it survives changes to the momentum window and the lattice size. Our results numerically demonstrate that the bulk geometry of a theory with an 𝒪(1) central charge, such as the 3D Ising model, is not captured by a single classical metric.
ER  -