While the three-dimensional Ising model has defied analytic solution, various numerical
methods like Monte Carlo, Monte Carlo renormalization group, and series expansion have …

We derive machine learning algorithms from discretized Euclidean field theories, making
inference and learning possible within dynamics described by quantum field theory …

The inverse renormalization group is studied based on the image super-resolution using the
deep convolutional neural networks. We consider the improved correlation configuration …

We present a physical interpretation of machine learning functions, opening up the
possibility to control properties of statistical systems via the inclusion of these functions in …

The key idea behind the renormalization group (RG) transformation is that properties of
physical systems with very different microscopic makeups can be characterized by a few …

We present a detailed study by Monte Carlo simulations and finite-size scaling analysis of
the phase diagram and ordered bulk phases for the three-dimensional Blume-Capel …

We use an m-vicinity method to examine Ising models on hypercube lattices of high
dimensions d≥ 3. This method is applicable for both short-range and long-range …

We develop a machine-learning renormalization group (MLRG) algorithm to explore and
analyze many-body lattice models in statistical physics. Using the representation learning …

We consider the non-perturbative renormalization group (RG) equations, obtained as
approximations of the exact Wetterich RG flow equation within the Blaizot–Mendez …

Classical liquid activity coefficient models, such as the nonrandom two‐liquid (NRTL) model,
fail near the critical point of the liquid–liquid equilibrium (LLE), unless a highly nonlinear …