用原型增强图神经网络，提升气候模拟预测精度
Prototype-enhanced prediction in graph neural networks for climate applications
- 引入输出原型作为额外输入，指导模型预测
- 使用随机原型即可提升性能，数据驱动选型可增效近10%
- 适合需要高精度气候模拟的科研与环保应用
数据驱动的模拟器正越来越多地用于学习和替代基于物理的仿真，以降低计算成本和运行时间。本文提出一种结构化方法，通过引入原型——即模拟器输出的近似值作为输入，来提升高维输出的预测质量。我们以大气扩散模拟为例，验证该方法在温室气体排放监测中的应用效果。对比基线模型，加入原型输入的模型表现更优，即使原型数量少或随机选取也有效；而通过数据驱动方法（如k-means）选择原型，某些指标性能可提升近10%。
原文摘要 · Abstract (English)
Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to improve the quality of these high-dimensional emulated outputs, through the use of prototypes: an approximation of the emulator's output passed as an input, which informs the model and leads to better predictions. We demonstrate our approach to emulate atmospheric dispersion, key for greenhouse gas emissions monitoring, by comparing a baseline model to models trained using prototypes as an additional input. The prototype models achieve better performance, even with few prototypes and even if they are chosen at random, but we show that choosing the prototypes through data-driven methods (k-means) can lead to almost 10\% increased performance in some metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。