3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions
This work addresses a domain-specific problem for astrophysics researchers by providing incremental improvements in emulator efficiency for PDR simulations.
The authors tackled the computational bottleneck of simulating photodissociation regions (PDRs) by creating a dataset of 8192 3D-PDR models and benchmarking neural differential equation emulators, achieving fast and robust emulators for integration into 3D simulations.
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.