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A Neural Network Subgrid Model of the Early Stages of Planet Formation
Thomas Pfeil · Miles Cranmer · Shirley Ho · Philip Armitage · Tilman Birnstiel · Hubert Klahr

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in Machine Learning and the Physical Sciences »

Planet formation is a multi-scale process in which the coagulation of μm-sizeddust grains in protoplanetary disks is strongly influenced by the hydrodynamicprocesses on scales of astronomical units (≈ 1.5 × 10^9 km). Studies are thereforedependent on subgrid models to emulate the micro physics of dust coagulationon top of a large scale hydrodynamic simulation. Numerical simulations whichinclude 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 modelcaptures details of the dust coagulation process that were so far not tractable withother dust coagulation prescriptions with similar computational efficiency

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Author Information

Thomas Pfeil (University Observatory Munich, University of Munich (LMU))
Miles Cranmer (Princeton University)

Miles Cranmer is an Astro PhD candidate trying to accelerate astrophysics with AI. Miles is from Canada and did his undergraduate in Physics at McGill. He is deeply interested in the automation of science, particularly aspects that are not yet tractable with existing machine learning, such as experiment planning, simulation, and theory. He works on symbolic regression, graph neural networks, normalizing flows, and learned simulation. He is hugely interested in symbolic ML, since, as he argues, symbolic models seem to be a surprisingly efficient basis for describing our universe.

Shirley Ho (Flatiron Institute)

Shirley Ho is a group leader and acting director at Flatiron Institute at Simons foundation, a research professor of physics and an affiliated faculty at Center for Data Science at NYU. Ho also holds associate (adjunct) professorship at Carnegie Mellon University and visiting appointment at Princeton University. She was a senior scientist at Berkeley National Lab from 2016-2018 and a Cooper-Siegel Development chair professor at Carnegie Mellon University before that. Ho was a Seaborg and Chamberlain Fellow from 2008-2011 at Berkeley Lab, after receiving her PhD in Astrophysics from Princeton University in 2008 under supervision of David Spergel. Ho graduated summa cum laude with a B.A. in Physics and a B.A. in Computer Science from UC Berkeley. A cited expert in cosmology, machine learning applications in astrophysics and data science,her interests are using deep learning accelerated simulations to understand the Universe, and other astrophysical phenomena. She tries her best to balance her love for the Universe, the machine and life especially during these crazy times.

Philip Armitage (Flatiron Institute)
Tilman Birnstiel (University Observatory, LMU, Munich, Germany)
Hubert Klahr (MPIA, Heidelberg)

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