学习通过图形神经网络模拟气溶动力学
Fabiana Ferracina1,2, Payton Beeler1, Mahantesh Halappanavar3
1Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
概括
我们开发了基于图形的气溶动力学学习 (GLAD),以加快气候模型的速度. 这种机器学习方法准确地模拟了气溶颗粒的行为,改善了气候和空气质量预测.
科学领域:
- 大气科学 大气科学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 气溶颗粒显著影响气候,天气和空气质量.
- 准确建模多样化和不断演变的粒子特性需要计算密集的粒子解析模型.
- 现有的模型在捕获详细的气溶动态的计算成本方面扎.
研究的目的:
- 为了加速计算上昂贵的颗粒分辨率的气溶模型.
- 引入一种新的机器学习框架,即基于图形的气溶动力学学习 (GLAD),用于气溶模拟.
- 训练一个替代模型来预测气溶的微物理和化学.
主要方法:
- 实现了一个基于图形网络的模拟器 (GNS),其中粒子是图形节点.
- 使用PartMC-MOSAIC粒子解析模型的输出训练了一个图形神经网络 (GNN).
- 模拟的气溶动力学,包括硫酸对混合成分颗粒的凝结.
主要成果:
- 经过训练的GNN准确地学习了化学动力学,并在不同的场景中进行了概括.
- 与传统模型相比,实现了高效的训练和预测时间.
- 证明了GLAD框架的稳定性和适应性.
结论:
- GLAD提供了一种高效准确的模拟气溶动态的方法.
- 这种机器学习方法可以显著加速气候和空气质量建模.
- 该框架显示,它有望促进我们对气溶微物理和化学的理解.
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