III: Small: Collaborative Research: Study of Neural Architectural Components in Physics-Informed Deep Neural Networks for Extreme Flood Prediction

理解我们的物质世界显然对人类社会是至关重要和有益的，这已经成为几个世纪以来许多科学和工程领域的中心焦点和挑战。该项目将开发基于机器学习的技术来模拟复杂的大气系统（从天气到气候）。大气系统模型可以近似大气流量并预测包括洪水在内的极端降水事件序列。洪水是世界上最致命、最昂贵的自然灾害之一。灾难性洪水造成的日益严重的损失促使各国加紧努力，通过提供早期预警来加强对灾难性洪水事件的准备和反应。该项目的研究结果将有助于决策者更好地确定特定政策行动的必要性和结果。例如，在洪水预测中，10-15天的提前期将允许水库运行规则的执行方式发生重大变化，以最大限度地减少洪水事件的影响。此外，该项目将为本科生和研究生提供宝贵的研究和培训机会，鼓励少数民族和妇女参与科学和工程，并对计算机科学课程和课件的开发产生广泛和可持续的影响。许多物理系统可以用一组控制偏微分方程来描述。然而，这些潜在的控制偏微分方程通常是耦合的和非线性的，没有易于处理的解析解，并且需要对初始条件和边界条件高度敏感的数值近似。该项目将当前对物理系统的理解与新颖的神经架构相结合，开发深度神经网络模型，以改善复杂物理系统模型的解释、泛化和预测。为了实现这一目标，本项目重点开展了三个相互关联的研究活动：(1)开发神经架构组件库，构建模块化神经网络模型；(2)测试基于神经结构构件的深度学习洪水预测方法；(3)建立物理启发的深度学习模型，以更好地解释和预测。该项目研究了一种开发和使用基本神经架构组件来构建大型物理信息深度神经网络的新方法。基于模块化的神经结构研究方法对于提高深度学习模型的可理解性和可解释性至关重要，在多个科学领域具有广泛的应用。从科学的角度来看，它将通过结合基于偏微分方程的数值天气预报模型的优势和深度学习的最新进展，为使用神经架构组件构建物理信息深度神经网络模型的有效性提供一个新的基准，并量化一类降水和洪水事件的可预测极限。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。

Understanding our physical world is clearly critical and beneficial to human society, which has become a central focus and challenge in many areas of science and engineering for centuries. This project will develop machine learning-based techniques to model complex atmospheric systems (from weather to climate). Atmospheric system models can approximate atmospheric flow and predict sequence of extreme precipitation events including flooding. Flooding is one the most deadly and costly natural hazards in the world. Mounting losses from catastrophic floods are driving an intense effort to increase preparedness and improve response to disastrous flood events by providing early warnings. Findings in this project will help decision makers better determine the need for and outcomes of particular policy actions. For example, a 10-15 day lead time in flood prediction will allow significant changes in the way reservoir operation rules are executed to minimize the impact of flood events. Moreover, this project will provide undergraduate and graduate students with valuable research and training opportunities, encourage minority and woman participation in science and engineering, and have a broad and sustainable impact on Computer Science curricula and courseware development. Many physical systems can be described by a set of governing partial differential equations. However, these underlying governing partial differential equations are often coupled and nonlinear, do not have tractable analytical solutions, and need numerical approximations that are highly sensitive to initial and boundary conditions. This project synthesizes current understanding of physical systems with novel neural architectures to develop deep neural network models that can improve interpretation, generalization and prediction of complex physical system models. To achieve this goal, this project focuses on three interrelated research activities: (1) developing a library of neural architectural components to build modular neural network models; (2) testing neural architectural component based deep learning approach for flood prediction; and (3) building physics inspired deep learning models for better interpretation and prediction. This project investigates a new approach of developing and using basic neural architectural components to build large physics-informed deep neural networks. The modularity-based approach on study of neural architectures is critically important to enhance understanding and interpretability of deep learning models and has broad applications in multiple scientific domains. From scientific perspective, it will provide a new benchmark on the efficacy of using neural architectural components to build physics-informed deep neural network models and quantify achievable predictability limits for a class of precipitation and flood events by combining strengths of partial differential equation based numerical weather prediction models and recent advances in deep learning.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.