EAGER: Scalable Climate Modeling using Message-Passing Recurrent Neural Networks

现实世界的气候模型往往严重依赖大规模的物理驱动的气候模型，涉及数百万未知参数和稀疏的真实世界测量来准确校准这些模型。这一建议旨在开发消息传递递归神经网络(MPRNN)，这是一个深度图神经网络框架，用于根据稀疏的时空传感器测量数据进行准确、可扩展和高效的气候建模。与模拟跨越空间和时间的连续行为的基于物理的细粒度气候模型不同，MPRNN利用在不同空间节点建立的异质递归神经网络的离散、分布式集合，这些网络模拟基于物理的底层模型并使用消息传递算法进行通信，以生成实时时空气候地图。这一建议在包括计算机科学、复杂系统和气候科学在内的几个子学科中做出了基础性贡献，包括：(I)设计可扩展的图机学习框架来模拟复杂气候系统，(Ii)使用消息传递算法和图神经网络来模拟基于基本偏微分方程组的物理信息时空模型，以及(Iii)设计一个通用库来高效地实现基于MPRNN的复杂气候系统模拟器。这项提议旨在展示MPRNN在多种气候建模工作中的有效性，包括对重力波进行建模、环境污染预测以及了解密集城市环境中气候变化的局部影响。这一建议建立在调查人员先前工作的基础上，这些工作为污染预测和重力波模拟提供了MPRNN的基线实施。这项工作对气候研究的各种利益相关者产生了更广泛的影响，包括气候建模研究人员和应对气候变化、空气污染和重力波等重要问题的政策专家。通过将基于物理的领域结构融入到深度图模型中，该建议可以使气候专家使用可伸缩的基于深度图的气候模型来有效地模拟基于物理的大型模拟模型的行为。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Real-world climate models have often been heavily reliant on large-scale physics-driven climate models involving millions of unknown parameters and sparse real-world measurements to accurately calibrate these models. This proposal aims to develop Message Passing Recurrent Neural Networks (MPRNN), a deep graph neural framework for accurate, scalable and efficient climate modeling from sparse spatio-temporal sensor measurements. Unlike fine-grained physics based climate models that model continuous behavior across space and time, MPRNNs leverage a discrete, distributed collection of heterogeneous recurrent neural networks established at different spatial nodes that simulate the underlying physics-based model and communicate using message passing algorithms to generate real-time spatio-temporal climate maps. This proposal makes fundamental contributions across several sub-disciplines including computer science, complex systems, and climate sciences including: (i) designing scalable graph machine learning frameworks for modeling complex climate systems, (ii) simulating underlying partial differential equations based physics-informed spatio-temporal models using message passing algorithms and graph neural networks, and (iii) designing a general purpose library for efficiently implementing MPRNN based simulators for complex climate systems. This proposal aims to demonstrate the efficacy of MPRNN on multiple climate modeling efforts including modeling gravity waves, environmental pollution forecasting and understanding the localized impact of climate variations in dense urban environments. This proposal builds upon prior work by the investigators that provides a baseline implementation of MPRNN for pollution forecasting and gravity wave modeling. This line of work has a broader impact on various stakeholders of climate research, including climate modeling researchers and policy experts in tackling important issues like climate change, air pollution and gravity waves. By incorporating physics-based domain structure into deep graph models, this proposal can enable climate experts to effectively emulate the behavior of large physics based simulation models using scalable deep graph based climate models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.