Studying the formation of large-scale structures (LSS) provides an opportunity to test our understanding of multiple aspects of cosmology – such as the nature of dark energy (DE), and the nature of dark matter (DM). One can constrain the elusive set of Modified Gravity (MG) theories with screening mechanisms, theories which can evade classical tests of General Relativity (GR). One can also pare down competing dark matter paradigms. Obtaining theoretical predictions on small scales for extended cosmologies often involves slow, non-linear numerics. I will discuss my work in developing rapid methods for simulating LSS formation, by informing simulation methods with analytic approximations. An abbreviation, if you will, of the solution-finding process. Informing numerics with analytics combines many approaches, and can be applied in many contexts – and so this talk will feature many abbreviated terms too. One of them being the Hi-COLA code I developed, to rapidly simulate structure formation in Horndeski gravity. Yet it does not stop there: extracting information from simulations is also a kind of abbreviation. I will also touch on the prospects of improving the understanding we gain from data by going beyond 2-point statistics.