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

淡水在全球经济、粮食、水和能源网络中发挥着重要作用，但由于对淡水生态系统服务需求的增加和气候变化的压力，淡水生态系统不断退化。及时监测水的性质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大挑战，如干旱、洪水和水安全。此外，水的温度和流量等水的性质信息有助于更好地了解水循环的相关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来捕获空间和时间上复杂的水动态提供了巨大的机会。特别是，图神经网络在模拟大型流域中溪流之间的相互作用方面显示出很大的希望。然而，在缺乏基础物理知识的情况下，现有基于图的模型的直接应用在捕捉复杂的与水有关的过程、模拟由人类基础设施或气候变化引起的数据分布变化以及从数据样本的缺乏中学习方面仍然有限。为了克服这些限制，该项目将探索图网络模型与物理知识的深度耦合，以模拟淡水生态系统中复杂、非平稳、观测不足的水动力学。本项目将为来自不同背景的研究生和本科生提供研究机会，研究结果将纳入课程开发。本项目旨在通过设计新的模型架构、学习策略和初始化方法来开发新的物理引导图网络模型。该项目还将探索利用物理知识的不同方式，既可以直接将物理与已知的数学方程相结合，也可以间接利用现有基于物理的模型中体现的知识。特别地，在这个项目中有三个创新。首先，将开发新的基于图形的体系结构来模拟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，以模拟由新增基础设施和气候变化引起的长期系统演化。第三，将开发新的模型初始化方法，将知识从现有的基于物理的模型转移到所提出的图网络模型中，以促进在数据稀缺场景中学习物理一致的模式。该奖项反映了美国国家科学基金会的法定使命，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。

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.