对于大气化学物质ODE的神经网络模拟器
Zhi-Song Liu1, Petri Clusius2, Michael Boy3
1School of Engineering Sciences, Lappeenranta-Lahti University of Technology LUT, Lahti, 15110, Finland; Atmospheric Modelling Centre Lahti, Lahti University Campus, Lahti, 15140, Finland.
概括
我们开发了一个神经网络模拟器ChemNNE,以快速模拟大气化学. 这种方法显著提高了预测化学度的计算速度和准确性.
科学领域:
- 大气化学 大气化学
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 大气化学模型是计算密集的.
- 深度神经网络在复杂的建模任务中表现有前途.
研究的目的:
- 为大气化学开发一个快速而准确的神经网络模拟器.
- 使用神经网络将大气化学建模为依赖时间的普通微分方程 (ODE).
主要方法:
- 提出ChemNNE,一个基于注意力的神经网络模拟器 (NNE).
- 用于时间模式的正弦时间嵌入和用于ODE建模的Fourier神经运算符.
- 为训练引入了三个基于物理的损失函数.
主要成果:
- 在建模准确度方面取得了最先进的性能.
- 在计算速度方面取得了显著的改进.
- 引入了用于基准测试的新型大规模数据集.
结论:
- ChemNNE为大气化学建模提供了一种高效准确的解决方案.
- 基于物理学的方法提高了模型的可靠性.
- 开发的数据集有助于在该领域的未来研究.
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