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Complex networks with pairwise connections have been vastly used for the modeling of interactions within systems. Although these type of models are capable to capture rich structures and different phases within a great variety of situations, their lack of explicit higher order interactions might result, in some contexts, limited. In this work a stochastic epidemic model on a simplicial complex is defined, generalizing the known Markovian SIR epidemic process on networks. By direct simulations and mean field analysis is shown that already by the introduction of third order interactions through a simplex, the SIR model can display important differences in its dynamics and stationary states with respect to the pairwise version of the model.

The inclusion of a fragmentation mechanism in population balance equations introduces complex interactions that make the analytical or even computational treatment much more difficult than for the pure aggregation case. This is specially true when variable sized fragments are allowed, because of the exponential growth in fragments size combinations with the number of monomers in the exchanges. In this contribution we present a new model that incorporates an instability threshold in the clusters, which induces arbitrary losses or gains of particles by fracture with a substantial simplification of the combinatorics of the process. The model exhibits two different regimes.

A novel formulation and training procedure for full Boltzmann machines in terms of a mixed binary quadratic feasibility problem is given. As a proof of concept, the theory is analytically and numerically tested on XOR patterns.