Simulating quantum magnetic systems such as frustrated spin models is a notoriously difficult problem, as it requires solving a complex quantum many-body problem. Despite a plethora of powerful existing approaches such as tensor network methods and extensive efforts to develop new techniques, each method also has its limitations such that, overall, there is still a significant lack of methods. The PFFRG is a complementary technique for simulating quantum magnetic systems which relies on a renormalization group ansatz. The key advantage over other methods lies in its enhanced flexibility as it allows one to treat systems with complex microscopic spin interactions and complicated lattice geometries, including three-dimensional networks. From a technical viewpoint, the approach amounts to solving large sets of coupled differential equations for spin correlation functions, whose numerical outcomes can be directly compared with experiments.
Our activities in developing, implementing and applying the PFFRG code are relatively young and have mostly occurred within the past ten years. Particularly, with the recent development of the PMFRG, which is a variant of the PFFRG, a new promising future research direction is opened up. Among other advantages, the PMFRG is capable of simulating the effects of finite temperatures more accurately. Given this fast progress in fundamental method development there is now a need for focussing more on our research codes. Particularly, our future activities aim at optimizing the code performance and making it more user friendly. Another important task is a better code optimization for high performance computing via efficient parallelization.