2025

Liu Zhi-Song; Clusius Petri; Boy Michael

Modelling 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 modelling. 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 capture temporal patterns in chemical concentration changes, we implement sinusoidal time embedding to represent periodic tendencies over time. Additionally, we leverage the Fourier neural operator to model the ODE process, enhancing computational efficiency and facilitating the learning of complex dynamical behavior. We introduce three physics-informed loss functions, targeting conservation laws and reaction rate constraints, to guide the training optimization process. To evaluate our model, we introduce a unique, large-scale chemical dataset designed for neural network training and validation, which can serve as a benchmark for future studies. The extensive experiments show that our approach achieves state-of-the-art performance in modelling accuracy and computational speed.