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的需求不断增加和气候变化带来的压力，淡水生态系统继续退化。及时监测水的特性可以为妥善的政策和管理决策提供有用的信息，以应对干旱、洪水和水安全等与水有关的重大挑战。此外，水的性质，如水温和流量的信息，可以帮助更好地了解相关的水循环中的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的复杂水动态提供了巨大的机会。特别是，图神经网络已经显示出巨大的潜力，在大型河流流域的流之间的相互作用建模。然而，在缺乏基础物理知识的情况下，现有基于图形的模型的直接应用仍然局限于捕获复杂的与水相关的过程，对人类基础设施或气候变化引起的数据分布变化进行建模，以及从缺乏的数据样本中学习。为了克服这些局限性，该项目将探索图形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。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。该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指导图网络模型。该项目还将探索利用物理知识的不同方法，既可以直接将物理学与已知的数学方程相结合，也可以间接利用现有物理模型中所包含的知识。特别是，在这个项目中有三个创新。首先，将开发新的基于图的架构，以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，以模拟由新增加的基础设施和气候变化引起的长期系统演化。第三，将通过将现有基于物理的模型的知识转移到拟议的图形网络模型来开发新的模型初始化方法，以促进在数据稀缺的情况下学习物理上一致的模式。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.