91 citations · 519 across the 32 of their papers we have counts for
32 papers
Kinetic and canonical momentum broadening in the Glasma
Dana Avramescu, Carlos Lamas, Tuomas Lappi +2
We lay the foundations for a quantum formalism describing the real-time evolution of particles in the Glasma phase of a heavy-ion collision, focusing on the implications of gauge i…
Uncovering Magnetic Phases with Synthetic Data and Physics-Informed Training
Agustin Medina, Marcelo Arlego, Carlos A. Lamas
We investigate the efficient learning of magnetic phases using artificial neural networks trained on synthetic data, combining computational simplicity with physics-informed strate…
Minimalist Neural Networks training for phase classification in diluted-Ising models
G. L. Garcia Pavioni, M. Arlego, C. A. Lamas
In this article, we explore the potential of artificial neural networks, which are trained using an exceptionally simplified catalog of ideal configurations encompassing both order…
On the neural network flow of spin configurations
Santiago Acevedo, Carlos A. Lamas, Alejo Costa Duran +2
We study the so-called neural network flow of spin configurations in the 2-d Ising ferromagnet. This flow is generated by successive reconstructions of spin configurations, obtaine…
An effective field theory approach for the bilayer honeycomb antiferromagnet
S. Acevedo, C. A. Lamas, P. Pujol
The spin-3/2 Heisenberg antiferromagnet on the bilayer honeycomb lattice is a minimal model to describe the magnetic behavior of BiMnO(NO). We study this model w…
Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning
S. Acevedo, M. Arlego, C. A. Lamas
We apply unsupervised learning techniques to classify the different phases of the antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram o…