用8192组模拟数据训练神经微分方程，加速星云光解区建模。
3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions
- 用神经微分方程构建光解区快速模拟器
- 在8192组3D-PDR模拟上验证，速度提升显著
- 适合天体物理模拟提速，尤其需频繁迭代的场景
本文发布了基于猎户座条状星云（Orion Bar）的光解区（PDR）三维模拟数据集，包含8192组不同初始条件的仿真结果。由于需同时追踪热平衡与化学组成，传统数值模拟计算成本极高，常成为3D模拟的瓶颈。为此，我们构建并对比了多种神经网络架构，重点评估基于增强型神经常微分方程（ANODE）的模型。结果表明，该方法可生成快速且鲁棒的代理模型，可用于经典代码的预处理步骤或直接作为完整替代，显著提升大规模3D PDR模拟效率。代码已开源（https://github.com/uclchem/neuralpdr）。
原文摘要 · Abstract (English)
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.
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