The inverse problem beyond two-body interaction: the cubic mean-field Ising model

Abstract

In this paper we solve the inverse problem for the cubic mean-field Ising model. Starting from configuration data generated according to the distribution of the model we reconstruct the free parameters of the system. We test the robustness of this inversion procedure both in the region of uniqueness of the solutions and in the region where multiple thermodynamics phases are present.

References in corpus (17)

• Weak pairwise correlations imply strongly correlated network states in a neural population
• Direct-coupling analysis of residue co-evolution captures native contacts across many protein families
• Networks beyond pairwise interactions: structure and dynamics
• The physics of higher-order interactions in complex systems
• Evolutionary dynamics of higher-order interactions in social networks
• Simplicial Closure and higher-order link prediction
• Dynamics on higher-order networks: A review
• Inverse statistical problems: from the inverse Ising problem to data science
• Mean-field theory for the inverse Ising problem at low temperatures
• An analysis of a large dataset on immigrant integration in Spain. The Statistical Mechanics perspective on Social Action
• Inverse problem robustness for multi-species mean field spin models
• Higher-order interactions in statistical physics and machine learning: A model-independent solution to the inverse problem at equilibrium
• Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states
• Human-AI ecosystem with abrupt changes as a function of the composition
• Cluster vs Single-Spin Algorithms -- Which are More Efficient?
• Inverse problem for multi-species mean field models in the low temperature phase
• Inverse problem for the mean-field monomer-dimer model with attractive interaction