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

III：小型：协作研究：用于极端洪水预测的物理信息深度神经网络中的神经架构组件研究

• 批准号: 2008202
• 负责人: Ping Chen
• 金额: $ 29.92万
• 依托单位: University of Massachusetts Boston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008202&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Study

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.

了解我们的物理世界显然对人类社会至关重要且有益，几个世纪以来，这已成为科学和工程许多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的序列。洪水是世界上最致命和代价最高的自然灾害之一。灾难性洪水造成的损失不断增加，促使人们大力加强准备工作，并通过提供早期预警来改善对灾难性洪水事件的响应。该项目的研究结果将帮助决策者更好地确定特定政策行动的需求和结果。例如，洪水预测提前 10-15 天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，鼓励少数族裔和女性参与科学和工程，并对计算机科学课程和课件开发产生广泛和可持续的影响。许多物理系统可以通过一组控制偏微分方程来描述。然而，这些基础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。该项目综合了当前对物理系统的理解与新颖的神经架构，以开发深度神经网络模型，可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，该项目重点开展三项相互关联的研究活动：（1）开发神经架构组件库以构建模块化神经网络模型； (2) 测试基于神经架构组件的洪水预测深度学习方法； (3) 构建受物理学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知识深度神经网络的新方法。基于模块化的神经架构研究方法对于增强深度学习模型的理解和可解释性至关重要，并且在多个科学领域具有广泛的应用。从科学的角度来看，它将为使用神经架构组件构建基于物理的深度神经网络模型的有效性提供新的基准，并通过结合基于偏微分方程的数值天气的优势来量化一类降水和洪水事件的可实现的可预测性限制预测模型和深度学习的最新进展。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Widening the Time Horizon: Predicting the Long-Term Behavior of Chaotic Systems

拓宽时间范围：预测混沌系统的长期行为

• DOI: 10.1109/icdm54844.2022.00094
• 发表时间: 2022-11-01
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Yong Zhuang;Matthew Almeida;Wei Ding;Patrick D Flynn;S. Islam;Ping Chen
• 通讯作者: Ping Chen