2022/11/08 by Thomas Pfeil, Pfeil, Thomas, Miles Cranmer +9
Chemical Engineering · Physics and Astronomy · #Advanced Combustion Engine Technologies #Astro and Planetary Science #Astrophysics and Star Formation Studies #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2211.04160
openalex publication_date 2022/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Planet formation is a multi-scale process in which the coagulation of μm-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units (≈ 1.5× 108 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.