Abstract:
Understanding the (near-)critical behavior of lattice models is one of the main challenges in statistical mechanics. A key approach to this problem is the computation of the model’s critical exponents. However, this task is generally difficult due to the intricate interplay between the specific features of the models and the geometry of the graphs on which they are defined.
A striking observation was made in the case of models defined on Z^d: beyond an upper-critical dimension 
, the geometry no longer plays a significant role, and the critical exponents simplify, matching those found on Cayley trees or complete graphs. The regime 
is called the mean-field regime of the model.
In the 1980’s, two prominent approaches have been developed to understand the mean-field regime of a wide class of models: the rigorous renormalization group method and the lace expansion.
We revisit the study of the mean-field regime and present an alternative, more probabilistic approach. Our method applies to various perturbative settings including (sufficiently) spread-out Bernoulli Percolation in dimensions 
.
Based on joint works with Hugo Duminil-Copin.
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