AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES)
AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES)
批准号:
2019758
负责人:
Amy McGovern
金额:
$1999.86万
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-09-01 至 2025-08-31
中文摘要
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英文摘要
Changes in weather patterns, oceans, sea level rise, and disaster risk amplify the need for accelerated research in both AI and Environmental Science (ES). The AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES) is a convergent, multi-sector institute that brings together researchers in AI, atmospheric science, ocean science, and risk communication to develop user-driven trustworthy AI that addresses the diverse data and research needs of pressing environmental concerns. AI2ES leverages dedicated partnerships in academia, government, and private industry to multiply the strategic impact and societal benefit of the institute’s groundbreaking integrated research in trustworthy AI, ES, and risk communication. By directly engaging environmental scientists and risk managers, AI2ES will improve the Nation’s understanding of severe weather and ocean phenomena, will save lives and property, and will increase societal resilience to climate change. As a National AI Research Institute, AI2ES brings together AI researchers, environmental scientists, and risk communication researchers to work synergistically, contributing to fundamental scientific advances in AI, social science, and environmental science. Researchers at AI2ES are investigating novel trustworthy AI techniques including techniques for physically-constrained machine learning, model interpretation and visualization for spatiotemporal data, uncertainty quantification, and robustness with adversarial data. In environmental science, AI2ES is significantly enhancing the understanding and prediction of high-impact atmospheric and ocean science phenomena at time scales ranging from hours to months. Integration of a solid theoretical framework for risk communication will grounds AI2ES in expert and professional end users’ needs, while simultaneously improving the risk communication community’s understanding of how risk communication approaches influence experts’ trust in AI-based methods and their willingness to integrate them into their workflow. AI2ES further serves society through its education and workforce development activities. The institute is dedicated to developing a skilled and diverse future workforce through deeply integrated activities that advance education, broaden participation and prepare future AI experts through exciting new programs such as AI certificate programs aimed at teaching AI to new experts needed in the workforce of the future. The institute is committed to broadening participation by creating a pipeline for underrepresented students in different parts of the country, starting at an HSI in South Texas (TAMU Corpus Christi) and its partner community college, Del Mar College, with a concurrent national outreach. AI2ES also engages private industry partners as well as collaboration with the National Center for Atmospheric Research (NCAR) to create a novel internship and mentoring program for underrepresented groups and develop unique workforce retraining modules for all ages that will engage users in learning AI for environmental applications familiar to all. AI2ES is providing both the research and the future workforce to deliver the advances needed for trustworthy prediction, understanding, and communication of the high-impact environmental hazards that are of concern to the entire country.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.
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Controlled Abstention Neural Networks for Identifying Skillful Predictions for Regression Problems
用于识别回归问题的熟练预测的受控弃权神经网络
DOI:
10.1029/2021ms002575
发表时间:
2021
期刊:
Journal of Advances in Modeling Earth Systems
影响因子:
6.8
作者:
[Barnes, Elizabeth A., Barnes, Randal J.]
通讯作者:
Barnes, Randal J.
Development and Investigation of GridRad-Severe, a Multiyear Severe Event Radar Dataset
多年严重事件雷达数据集 GridRad-Severe 的开发和研究
DOI:
10.1175/mwr-d-23-0017.1
发表时间:
2023
期刊:
Monthly Weather Review
影响因子:
3.2
作者:
[Murphy, Amanda M., Homeyer, Cameron R., Allen, Kiley Q.]
通讯作者:
Allen, Kiley Q.
DOI:
10.1017/eds.2022.7
发表时间:
2021-03
期刊:
Environmental Data Science
影响因子:
--
作者:
[Antonios Mamalakis;I. Ebert‐Uphoff;E. Barnes]
通讯作者:
Antonios Mamalakis;I. Ebert‐Uphoff;E. Barnes
Using deep learning to emulate and accelerate a radiative-transfer model
使用深度学习来模拟和加速辐射传输模型
DOI:
10.1175/jtech-d-21-0007.1
发表时间:
2021
期刊:
Journal of Atmospheric and Oceanic Technology
影响因子:
2.2
作者:
[Lagerquist, Ryan, Turner, David, Ebert-Uphoff, Imme, Stewart, Jebb, Hagerty, Venita]
通讯作者:
Hagerty, Venita
Diagnosing Storm Mode with Deep Learning in Convection-Allowing Models
利用允许对流模型中的深度学习诊断风暴模式
DOI:
10.1175/mwr-d-22-0342.1
发表时间:
2023
期刊:
Monthly Weather Review
影响因子:
3.2
作者:
[Sobash, Ryan A., Gagne, David John, Becker, Charlie L., Ahijevych, David, Gantos, Gabrielle N., Schwartz, Craig S.]
通讯作者:
Schwartz, Craig S.
共 17 条
Collaborative Research: Conference: NSF Workshop Sustainable Computing for Sustainability
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批准号:2334855
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项目类别:Standard Grant
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资助金额:$0.14万
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财政年份:2023
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负责人:Amy McGovern
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依托单位:
EAGER: Improving our Understanding of Supercell Storms through Data Science
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批准号:1802627
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项目类别:Standard Grant
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资助金额:$16.85万
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财政年份:2018
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负责人:Amy McGovern
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依托单位:
CAREER: Developing Dynamic Relational Models to Anticipate Tornado Formation
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批准号:0746816
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项目类别:Continuing Grant
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资助金额:$50.0万
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财政年份:2008
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负责人:Amy McGovern
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依托单位:
海外基金