Neural network emulator for atmospheric chemical ODE

• DOI: 10.1016/j.neunet.2024.107106
• Publication Date: 2025-01-02T17:17:04Z
• Authors: Zhi-Song Liu; Petri Clusius; Michael Boy
• Keywords: Chemical Physics (physics.chem-ph); FOS: Computer and information sciences; Computer Science - Machine Learning; Physics - Atmospheric and Oceanic Physics; Physics - Chemical Physics; Atmospheric and Oceanic Physics (physics.ao-ph); FOS: Physical sciences; Machine Learning (cs.LG)

25 pages, 8 figures<br/>Modeling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modeling. We consider atmospheric chemistry as a time-dependent Ordinary Differential Equation. To extract the hidden correlations between initial states and future time evolution, we propose ChemNNE, an Attention based Neural Network Emulator (NNE) that can model the atmospheric chemistry as a neural ODE process. To efficiently simulate the chemical changes, we propose the sinusoidal time embedding to estimate the oscillating tendency over time. More importantly, we use the Fourier neural operator to model the ODE process for efficient computation. We also propose three physical-informed losses to supervise the training optimization. To evaluate our model, we propose a large-scale chemical dataset that can be used for neural network training and evaluation. The extensive experiments show that our approach achieves state-of-the-art performance in modeling accuracy and computational speed.<br/>