A Neural Network Subgrid Model of the Early Stages of Planet Formation
This work addresses the computational bottleneck in simulating planet formation for astrophysicists, offering an incremental improvement in subgrid modeling.
The authors tackled the multi-scale problem of dust coagulation in planet formation by developing a fast and accurate neural network subgrid model trained on high-resolution simulations, which captures details previously intractable with similar computational efficiency.
Planet formation is a multi-scale process in which the coagulation of $\mathrm{μm}$-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.