activity
19952022
most citedThe 3-d Random Field Ising Model at zero temperature

69 citations · 333 across the 24 of their papers we have counts for

collaborators

24 papers

cond-mat.dis-nn2022

Random-bond antiferromagnetic Ising model in a field

Jean-Christian Anglès d'Auriac, Ferenc Iglói

Using combinatorial optimisation techniques we study the critical properties of the two- and the three-dimensional Ising model with uniformly distributed random antiferromagnetic c…

cond-mat.stat-mech2021★ 2 cited

Scattering Signatures of Invasion Percolation

Jean-Christian Anglès d'Auriac, Pierre-Etienne Wolf

Motivated by recent experiments, we investigate the scattering properties of percolation clusters generated by numerical simulations on a three dimensional cubic lattice. Individua…

cond-mat.stat-mech2021

Statistics of percolating clusters in a model of photosynthetic bacteria

Jean-Christian Anglès d'Auriac, Ferenc Iglói

In photosynthetic organisms, the energy of light during illumination is absorbed by the antenna complexes, which is transmitted by excitons and is either absorbed by the reaction c…

cond-mat.dis-nn2019★ 10 cited

Reentrant Random Quantum Ising Antiferromagnet

Péter Lajkó, Jean-Christian Anglès d'Auriac, Heiko Rieger +1

We consider the quantum Ising chain with uniformly distributed random antiferromagnetic couplings and uniformly distributed random transverse fields ($Γ_0 \le Γ…

hep-lat2011★ 1 cited

About the Yukawa model on a lattice in the quenched approximation

F. de Soto, Jean-Christian Anglès d'Auriac

We study the Yukawa model on a 4-dimensional Euclidean lattice in the quenched approximation. A particular attention is given to the singularities of the Dirac operator in the phas…

cond-mat.stat-mech2007★ 8 cited

Rounding of first-order phase transitions and optimal cooperation in scale-free networks

M. Karsai, J-Ch. Anglès d'Auriac, F. Iglói

We consider the ferromagnetic large- state Potts model in complex evolving networks, which is equivalent to an optimal cooperation problem, in which the agents try to optimize t…