Application of the Maximum relative Entropy method to the physics of ferromagnetic materials

Abstract

It is known that the Maximum relative Entropy (MrE) method can be used to both update and approximate probability distributions functions in statistical inference problems. In this manuscript, we apply the MrE method to infer magnetic properties of ferromagnetic materials. In addition to comparing our approach to more traditional methodologies based upon the Ising model and Mean Field Theory, we also test the effectiveness of the MrE method on conventionally unexplored ferromagnetic materials with defects.

27 pages, 5 figures, 1 table

References in corpus (9)

• Updating Probabilities
• Jacobi Fields on Statistical Manifolds of Negative Curvature
• Variational Principle of Bogoliubov and Generalized Mean Fields in Many-Particle Interacting Systems
• Can chaotic quantum energy levels statistics be characterized using information geometry and inference methods?
• Information Geometry, Inference Methods and Chaotic Energy Levels Statistics
• Using Relative Entropy to Find Optimal Approximations: an Application to Simple Fluids
• Information geometric complexity of a trivariate Gaussian statistical model
• Geometrodynamics of Information on Curved Statistical Manifolds and its Applications to Chaos
• Superfluid density near the critical temperature in the presence of random planar defects

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• Entropic Spectral Learning for Large-Scale Graphs
• Entropic Trace Estimates for Log Determinants
• An information geometric perspective on the complexity of macroscopic predictions arising from incomplete information
• Entropic Determinants