Détail du document
Identifiant
oai:arXiv.org:2412.00758
Sujet
Astrophysics - Astrophysics of Gal... Astrophysics - Instrumentation and... Astrophysics - Solar and Stellar A... Computer Science - Machine Learnin...Auteur
Vermariën, Gijs Viti, Serena Ravichandran, Rahul Bisbas, Thomas G.Catégorie
sciences : astrophysique
Année
2024
Date de référencement
04/12/2024
Mots clés
regions models dataset astrophysicsRésumé
We present a novel dataset of simulations of the photodissociation region (PDR) in the Orion Bar and provide benchmarks of emulators for the dataset.
Numerical models of PDRs are computationally expensive since the modeling of these changing regions requires resolving the thermal balance and chemical composition along a line-of-sight into an interstellar cloud.
This often makes it a bottleneck for 3D simulations of these regions.
In this work, we provide a dataset of 8192 models with different initial conditions simulated with 3D-PDR.
We then benchmark different architectures, focusing on Augmented Neural Ordinary Differential Equation (ANODE) based models (Code be found at https://github.com/uclchem/neuralpdr).
Obtaining fast and robust emulators that can be included as preconditioners of classical codes or full emulators into 3D simulations of PDRs.
;Comment: Accepted to NeurIPS Machine Learning and the Physical Sciences Workshop 2024
Vermariën, Gijs,Viti, Serena,Ravichandran, Rahul,Bisbas, Thomas G., 2024, 3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions
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