Collaborative Research: III: Small: Physics Guided Graph Networks for Modeling Water Dynamics in Freshwater Ecosystems

合作研究：III：小型：用于模拟淡水生态系统中水动力学的物理引导图网络

• 批准号: 2316305
• 负责人: Xiaowei Jia
• 金额: $ 35万
• 依托单位: University of Pittsburgh
• 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 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316305&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Small; Physics

Fresh water plays an important role for the global economic, food, water, and energy networks, but freshwater ecosystems continue to degrade due to pressures from increasing demands for freshwater ecosystem services and a shifting climate. Timely monitoring of water properties can provide useful information for sound policy and management decisions to address important water-related challenges such as droughts, floods, and water security. Moreover, the information of water properties such as water temperature and streamflow can help better understand relevant biogeochemical and ecological processes in the water cycle. The recent investment on large-scale water data repositories provides a tremendous opportunity for using machine learning to capture complex water dynamics over space and time. In particular, graph neural networks have shown great promise for modeling interactions amongst streams in large river basins. However, in the absence of underlying physical knowledge, direct applications of existing graph-based models remain limited in capturing complex water-related processes, modeling the shift of data distribution caused by human infrastructure or changing climate, and learning from a paucity of data samples. To overcome these limitations, this project will explore a deep coupling of graph network models with physical knowledge to model complex, non-stationary, poorly observed water dynamics in freshwater ecosystems. This project will provide research opportunities to graduate and undergraduate students from diverse backgrounds, and the results of this project will be incorporated into curriculum development. This project aims to develop new physics-guided graph network models by designing new model architectures, learning strategies, and initialization methods. This project will also explore different ways to leverage physical knowledge, both directly by integrating physics from known mathematical equations, and indirectly by making use of the knowledge embodied in existing physics-based models. In particular, there are three innovations that are pursued in this project. First, new graph-based architectures will be developed to model the complex nature of physical objects and the dynamic interactions between physical processes. Second, new graph-based continual learning strategies will be investigated to model long term system evolution caused by newly added infrastructure and changing climate. Third, new model initialization methods will be developed by transferring knowledge from existing physics-based models to the proposed graph network models to facilitate learning physically consistent patterns in data-scarce scenarios.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.

淡水对于全球经济，食品，水和能源网络起着重要作用，但是由于对淡水生态系统服务的需求不断增长和气候变化，淡水生态系统继续降低。及时监控水性能可以为合理的政策和管理决策提供有用的信息，以应对与水有关的重要挑战，例如干旱，洪水和水安全。此外，水温和水流等水性能的信息可以帮助更好地理解水周期中相关的生物地球化学和生态过程。 最近对大规模水数据存储库的投资为使用机器学习在时空上捕获复杂的水动力学提供了巨大的机会。尤其是，图神经网络在大型河流盆地中的流相互作用上表现出了巨大的希望。但是，在没有潜在的物理知识的情况下，现有基于图的模型的直接应用在捕获复杂的水相关过程时仍然有限，建模由人类基础设施或气候变化引起的数据分布的转移以及从数据样本的稀少度中学习。为了克服这些局限性，该项目将探索具有物理知识的图形网络模型的深入耦合，以建模淡水生态系统中的复杂，非平稳，观察到的水动力学。该项目将为来自不同背景的研究生和本科生提供研究机会，该项目的结果将纳入课程开发中。该项目旨在通过设计新的模型体系结构，学习策略和初始化方法来开发新的物理学指导网络模型。该项目还将通过直接通过从已知的数学方程式整合物理学，并通过利用现有基于物理学的模型中体现的知识来探索不同的方法来利用物理知识。特别是，该项目有三项创新。首先，将开发新的基于图的架构，以建模物理对象的复杂性质和物理过程之间的动态交互。其次，将研究新的基于图的持续学习策略，以模拟由新添加的基础架构和气候变化引起的长期系统演变。第三，将通过将知识从现有的基于物理的模型转移到拟议的图形网络模型来开发新的模型初始化方法，以促进在数据筛选方案中学习在物理一致的模式。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识绩效和更广泛影响的评估来通过评估来获得支持的。