GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity

GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity

Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds, galaxy formation, and the development of large-scale structures in the Universe. However, the nonlinear interaction be...

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Bibliographic Details
Main Authors: Sayantan Auddy, Ramit Dey, Neal J Turner, Shantanu Basu
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
hydrodynamics
star formation
gravitational instability
neural networks
machine learning
PINN
Online Access:https://doi.org/10.1088/2632-2153/ad3a32

Internet

https://doi.org/10.1088/2632-2153/ad3a32

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