NeuralPDR: Neural Differential Equations as surrogate models for Photodissociation Regions
This work addresses the need for faster astrochemical modeling to interpret high-resolution telescope data, enabling rapid statistical inference and higher-resolution simulations in astrophysics.
The authors tackled the high computational cost of simulating astrophysical environments by developing surrogate models using Latent Augmented Neural Ordinary Differential Equations, which achieved speedup and accurately reproduced observable column density maps from datasets including a 3D simulation.
Computational astrochemical models are essential for helping us interpret and understand the observations of different astrophysical environments. In the age of high-resolution telescopes such as JWST and ALMA, the substructure of many objects can be resolved, raising the need for astrochemical modeling at these smaller scales, meaning that the simulations of these objects need to include both the physics and chemistry to accurately model the observations. The computational cost of the simulations coupling both the three-dimensional hydrodynamics and chemistry is enormous, creating an opportunity for surrogate models that can effectively substitute the chemical solver. In this work we present surrogate models that can replace the original chemical code, namely Latent Augmented Neural Ordinary Differential Equations. We train these surrogate architectures on three datasets of increasing physical complexity, with the last dataset derived directly from a three-dimensional simulation of a molecular cloud using a Photodissociation Region (PDR) code, 3D-PDR. We show that these surrogate models can provide speedup and reproduce the original observable column density maps of the dataset. This enables the rapid inference of the chemistry (on the GPU), allowing for the faster statistical inference of observations or increasing the resolution in hydrodynamical simulations of astrophysical environments.