Simon Catterall and students have used PoP and Weasel to study quantum models of space and time and matter. They model space and time by a discrete triangulation, where geometry is allowed to fluctuate. They study how the fluctuating space-time geometry is affected by the addition of matter fields, calculating the fractal dimension of these models and the creation of "baby universes". Prof. Catterall also studies lattice formulations of supersymmetric field theories, which are believed to be important for predicting new physics at very high energies. These simulations are quite challenging as they contain non-local effective interactions.

Mark Bowick and students have used PoP to study membranes. These membrane models might be used to describe red blood cells or lipid bilayers, natural or artificial, for example. Bowick's group has also studied crystals on non-flat geometries (such as believed to be formed by electrons on the interior of small bubbles or by colloidal particles on the surface of oil drops in water.)

Alan Middleton and Cristina Marchetti have used PoP and Weasel to study the static and dynamic behavior of disordered materials and the links between computer science algorithms and physical models. The simulations are of two types:

1. Dynamical simulations: We are interested in the different types of flow (plastic, elastic, or channel, e.g.) that can take place and the transitions between them. The results can be applied to superconductors, arrays of small electronic dots, and even earthquakes, for example.
2. Optimization: The lowest-energy state of a complex, random material can be highly nontrivial and difficult to find. The physical problem can be mapped general problems studied in computer science; we apply these algorithms to study complex materials. In turn, we look for physical perspectives to understand and improve algorithms.

Rafael Sorkin's group (with external research support from the National Science Foundation) has also used PoP to study the causal set description of space and time. Here, space and time are modeled by sets of points connected in a causal structure (partially ordered sets.)