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
基本信息
- 批准号:2008202
- 负责人:
- 金额:$ 29.92万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
理解我们的物理世界显然对人类社会至关重要和有益,几个世纪以来,这已经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,以模拟复杂的大气系统(从天气到气候)。大气系统模式可以近似大气流动和预测极端降水事件,包括洪水序列。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,促使人们加紧努力,通过提供早期预警,加强备灾工作,改善对灾难性洪水事件的反应。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,洪水预测的10-15天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,这些基本的偏微分方程往往是耦合和非线性的,没有易于处理的解析解,并需要数值逼近,是高度敏感的初始和边界条件。该项目将当前对物理系统的理解与新的神经架构相结合,以开发深度神经网络模型,从而改善复杂物理系统模型的解释、推广和预测。为了实现这一目标,该项目侧重于三个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深度学习方法用于洪水预测;(3)构建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,它将为使用神经结构组件构建物理学的有效性提供一个新的基准-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,为一类降水和洪水事件建立了知情的深度神经网络模型,并量化了可实现的可预测性极限。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知识价值和更广泛的影响审查标准进行评估来提供支持。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Widening the Time Horizon: Predicting the Long-Term Behavior of Chaotic Systems
- DOI:10.1109/icdm54844.2022.00094
- 发表时间:2022-11
- 期刊:
- 影响因子:0
- 作者:Yong Zhuang;Matthew Almeida;Wei Ding;Patrick D Flynn;S. Islam;Ping Chen
- 通讯作者:Yong Zhuang;Matthew Almeida;Wei Ding;Patrick D Flynn;S. Islam;Ping Chen
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Ping Chen其他文献
Interactive Visualization of Large High-Dimensional Datasets
大型高维数据集的交互式可视化
- DOI:
- 发表时间:
2009 - 期刊:
- 影响因子:0
- 作者:
Wei Ding;Ping Chen - 通讯作者:
Ping Chen
[Detection of aspiration of nasopharyngeal secretion and the relationship between the aspiration of nasopharyngeal secretion and the incidence of pneumonia].
鼻咽分泌物误吸检测及鼻咽分泌物误吸与肺炎发病的关系[J].
- DOI:
10.3760/cma.j.issn.1001-0939.2015.07.010 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Ni Liu;Zeguang Zheng;Ping Chen;P. Hou;Xinni Wang;Hongyi Li;Rongchang Chen - 通讯作者:
Rongchang Chen
Dynamic Magnetic Properties of Electrospun NiZn Spinel Ferrite Nanofibers
电纺镍锌尖晶石铁氧体纳米纤维的动态磁性能
- DOI:
10.1109/tmag.2014.2325711 - 发表时间:
2014 - 期刊:
- 影响因子:2.1
- 作者:
Ping Chen;Ruixin Wu;J. Xiao - 通讯作者:
J. Xiao
α-hydroxyamide derived aminodiols as potent inhibitors of hiv protease
α-羟基酰胺衍生的氨基二醇作为艾滋病毒蛋白酶的有效抑制剂
- DOI:
10.1016/0960-894x(95)00293-3 - 发表时间:
1995 - 期刊:
- 影响因子:0
- 作者:
Saleem Ahmad;A. Ashfaq;M. Alam;G. Bisacchi;Ping Chen;P. Cheng;J. Greytok;M. Hermsmeier;P. Lin;Karen A. Lis;Z. Merchant;Toomas Mitt;M. Skoog;S. Spergel;J. Tino;G. Vite;R. Colonno;R. Zahler;J. Barrish - 通讯作者:
J. Barrish
Suppression of V-pits formation in InGaN layer by stepped growth with annealing interval
通过退火间隔阶梯生长抑制 InGaN 层中 V 坑的形成
- DOI:
10.1016/j.surfin.2021.101691 - 发表时间:
2022-02 - 期刊:
- 影响因子:6.2
- 作者:
Feng Liang;Degang Zhao;Zongshun Liu;Ping Chen;Jing Yang - 通讯作者:
Jing Yang
Ping Chen的其他文献
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{{ truncateString('Ping Chen', 18)}}的其他基金
Collaborative Research: EAGER: Deep Learning-based Multimodal Analysis of Sleep
合作研究:EAGER:基于深度学习的睡眠多模态分析
- 批准号:
2334665 - 财政年份:2023
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
III: Small: EAGER: Representation Learning of Connotation and Denotation Knowledge for Atomic Information Units
III:小:EAGER:原子信息单元的内涵和外延知识的表示学习
- 批准号:
1914489 - 财政年份:2019
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
Supporting U.S.-Based Students to Participate in the 2018 IEEE International Conference on Data Mining (ICDM 2018)
支持美国学生参加2018年IEEE数据挖掘国际会议(ICDM 2018)
- 批准号:
1836469 - 财政年份:2018
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
EAGER: Advanced Machine Learning Techniques to Discover Disease Subtypes in Cancer
EAGER:先进的机器学习技术发现癌症疾病亚型
- 批准号:
1743010 - 财政年份:2017
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
Collaborative Project: Enriching Security Curricula and Enhancing Awareness of Security in Computer Science and Beyond
合作项目:丰富安全课程并增强计算机科学及其他领域的安全意识
- 批准号:
1423915 - 财政年份:2014
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
Collaborative Project: Enriching Security Curricula and Enhancing Awareness of Security in Computer Science and Beyond
合作项目:丰富安全课程并增强计算机科学及其他领域的安全意识
- 批准号:
1241661 - 财政年份:2012
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
REU Site: Research Experiences in Algorithm Design and Analysis for Students in Undergraduate Institutions
REU网站:本科院校学生算法设计与分析研究经验
- 批准号:
0851984 - 财政年份:2009
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
Collaborative Research: An Interactive Undergraduate Data Mining Course with Industrial-Strength Projects
协作研究:具有工业强度项目的交互式本科数据挖掘课程
- 批准号:
0737408 - 财政年份:2008
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
Collaborative Research: Module-Based Computer Security Courses and Laboratory for Small and Medium Sized Universities
合作研究:中小型大学基于模块的计算机安全课程和实验室
- 批准号:
0311385 - 财政年份:2003
- 资助金额:
$ 29.92万 - 项目类别:
Standard Grant
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