Neural Network Emulator for Atmospheric Chemical ODE

2024-08-03

Neural Networks

AI Summary

High computational cost and inability to support real-time simulation hinder atmospheric chemical reaction modeling. To address this, we propose ChemNNE—a physics-informed neural ordinary differential equation (ODE) framework driven by attention mechanisms. Methodologically, it models species concentration evolution as a time-dependent ODE; incorporates sinusoidal time embeddings to capture diurnal and seasonal periodicity; enforces physical consistency via a composite loss function that jointly satisfies mass conservation, charge conservation, and stoichiometric constraints; and leverages Fourier neural operators to enhance long-range temporal dependency modeling. The model is trained on a large-scale, in-house chemical kinetics dataset. Experiments demonstrate that ChemNNE achieves state-of-the-art accuracy while accelerating inference by three to four orders of magnitude over conventional numerical solvers—enabling, for the first time, real-time, high-resolution, fully coupled simulation of atmospheric chemistry.

Technology Category

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• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

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Abstract

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 behaviour. 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.

Problem

• Airborne Chemical Reactions
• Computational Efficiency
• Simulation Accuracy

Innovation

• ChemNNE
• Sinusoidal Time Embedding
• Fourier Neural Operator