arXiv Machine Learning By Ejaaz Merali, Ryan Larson, Ehsan Khatami, Richard T. Scalettar

The Blume-Capel Model as a $\phi^6$ Field Theory: A Monte Carlo and Flow Model Study

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The paper presents Monte Carlo simulations of a continuous-spin Blume‑Capel model and maps its phase diagram in the crystal‑field–temperature plane, varying the spin softness. It demonstrates that normalizing flows—generative models enabling direct, autocorrelation‑free sampling—serve as effective samplers throughout the diagram, including first‑order transitions and near the tricritical point. Finite‑size scaling of flow-generated samples accurately identifies first‑, second‑order transitions and the tricritical point, matching the precision of conventional Monte Carlo methods.

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arXiv Machine Learning
Sep 4

Data Driven Equation Discovery for Phase-Ordering Dynamics : From Allen Cahn to the Ising Model

The paper investigates the use of PDE‑SINDy to discover governing equations for phase‑ordering dynamics, benchmarking against the Allen–Cahn equation and applying it to the Ising model with Glauber spin‑flip dynamics. It systematically studies how data availability, library size, and noise affect term identification and coefficient recovery, finding that stability‑selection with library bagging improves robustness. The recovered coarse‑grained equations successfully reproduce the characteristic phase‑separation and coarsening behavior of the microscopic Ising system.

By Partha Sarathi Mondal, Manav Kumar Jalan, Anish Kumar, Shradha Mishra