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

EAGER：使用消息传递循环神经网络进行可扩展的气候建模

• 批准号: 2335773
• 负责人: Lakshminarayan Subramanian
• 金额: $ 30万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335773&HistoricalAwards=false
• 关键词: EAGER; Scalable; Climate; Modeling; using

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.

现实世界的气候模型往往严重依赖于大规模的物理驱动的气候模型，涉及数百万个未知参数和稀疏的现实世界的测量，以准确地校准这些模型。该提案旨在开发消息传递递归神经网络（MPRNN），这是一种深度图神经框架，用于从稀疏时空传感器测量中进行准确，可扩展和高效的气候建模。与基于细粒度物理学的气候模型不同，MPRNN利用在不同空间节点建立的离散分布式异构递归神经网络集合，这些网络模拟基于物理学的基础模型，并使用消息传递算法进行通信，以生成实时时空气候地图。该提案为计算机科学、复杂系统和气候科学等多个子学科做出了重要贡献，包括：（i）设计用于模拟复杂气候系统的可扩展图机器学习框架，（ii）使用消息传递算法和图神经网络模拟基于偏微分方程的物理学时空模型，以及（iii）设计一个通用库，用于有效地实现复杂气候系统的基于MPRNN的模拟器。该提案旨在证明MPRNN在多种气候建模工作中的有效性，包括建模重力波，环境污染预测和了解密集城市环境中气候变化的局部影响。 该提案建立在研究人员之前的工作基础上，该工作为污染预测和重力波建模提供了MPRNN的基线实现。这项工作对气候研究的各个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政策专家，以解决气候变化，空气污染和重力波等重要问题。通过将基于物理学的领域结构纳入深度图模型，该提案可以使气候专家能够使用可扩展的基于深度图的气候模型有效地模拟基于物理学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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。