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Neural network emulator for atmospheric chemical ODE

Year of publication

2025

Authors

Liu Zhi-Song; Clusius Petri; Boy Michael

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 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.
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Organizations and authors

LUT University

Boy Michael Orcid -palvelun logo

Liu Zhisong Orcid -palvelun logo

Publication type

Publication format

Article

Parent publication type

Journal

Article type

Original article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A1 Journal article (refereed), original research

Publication channel information

Publisher

Elsevier

Volume

184

Article number

107106

​Publication forum

63865

Open access

Open access in the publisher’s service

No

Self-archived

No

Other information

Fields of science

Computer and information sciences

Keywords

[object Object],[object Object],[object Object],[object Object],[object Object]

Internationality of the publisher

International

International co-publication

No

Co-publication with a company

No

DOI

10.1016/j.neunet.2024.107106

The publication is included in the Ministry of Education and Culture’s Publication data collection

Yes