  Planet formation is a multi-scale process in which the coagulation of
$\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly
influenced by the hydrodynamic processes on scales of astronomical units
($\approx 1.5\times 10^8 \,\mathrm{km}$). Studies are therefore dependent on
subgrid models to emulate the micro physics of dust coagulation on top of a
large scale hydrodynamic simulation. Numerical simulations which include the
relevant physical effects are complex and computationally expensive. Here, we
present a fast and accurate learned effective model for dust coagulation,
trained on data from high resolution numerical coagulation simulations. Our
model captures details of the dust coagulation process that were so far not
tractable with other dust coagulation prescriptions with similar computational
efficiency.

Earth and Planetary Astrophysics;Machine Learning