3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 1 Dec 2024.
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9 of 25 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 584
- 2019Augmented Neural ODEscited by 396
- 2022On Neural Differential Equationscited by 264
- 2020Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problemscited by 45
- 2022Neural networks: solving the chemistry of the interstellar mediumcited by 7
- 2023Speeding up astrochemical reaction networks with autoencoders and neural ODEscited by 6
- 2024Emulating the interstellar medium chemistry with neural operatorscited by 5
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