NSF Convergence Accelerator: Symposium on Predicting Extremes by Data-Driven Analytics

NSF 融合加速器：通过数据驱动分析预测极端情况研讨会

• 批准号: 2035365
• 负责人: A Surjalal Sharma
• 金额: $ 10万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035365&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Symposium; Predicting

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This symposium on Predicting Extremes by Data-Driven Analytics will help identify topic areas for new tracks in the NSF Convergence Accelerator. Extreme events and associated hazards in natural, commercial, and security systems underlie the most devastating catastrophes in society. The societal risks arising from extreme events are very high both in terms of likelihood and impact on society. The increasing impact of disasters and the consequent risks have led leading organizations and institutions around the world to develop new approaches to mitigate the impacts and develop strategies for resilience. Improved predictive capability for extreme events is a critical scientific need in order to achieve effective disaster risk assessment, which is a product of three factors: the probability of the underlying events, vulnerability of the system and consequences therein. The understanding and modeling of extreme events contributes towards developing the probabilities of occurrence. This symposium will bring together participants from academia, government and industry to develop data-driven analytics as a pathway for predicting extreme events in natural and anthropogenic systems, including applications in terrestrial and space weather, finance and economics, and cybersecurity. The symposium is planned as a 3-day event to be run as a virtual meeting. It will emphasize participation by researchers and students from underrepresented communities that may be especially vulnerable to the effects of extreme events.An essential step toward achieving better predictability is uncertainty quantification, which is an inherently interdisciplinary endeavor, requiring convergence across multiple disciplines and participation by multiple stakeholders across academia, industry and government. A Convergence Accelerator track on this theme would benefit multiple sectors. Developing a framework for predicting extremes requires the harnessing of massive data from a variety of sources. The symposium will discuss the idea of developing a common platform for stakeholders in government, industry and nonprofits as part of this potential new track in the Convergence Accelerator. The themes of the symposium are of general interest due to their potential for high impact on society. A symposium proceedings will be produced, which will provide broad exposure to the potential outcomes of convergence research in this topic area.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.

NSF 融合加速器支持以使用为基础的、基于团队的多学科努力，以解决国家重大挑战，并将在不久的将来为社会提供有价值的成果。本次关于通过数据驱动分析预测极端情况的研讨会将有助于确定 NSF 融合加速器中新轨道的主题领域。自然、商业和安全系统中的极端事件和相关危害是社会上最具破坏性的灾难的根源。极端事件引发的社会风险无论是发生的可能性还是对社会的影响都非常高。灾害的影响日益加大以及随之而来的风险促使世界各地的领先组织和机构开发新方法来减轻影响并制定恢复战略。提高极端事件的预测能力是实现有效灾害风险评估的关键科学需求，它是三个因素的产物：潜在事件的概率、系统的脆弱性及其后果。对极端事件的理解和建模有助于确定发生的概率。本次研讨会将汇集来自学术界、政府和工业界的参与者，共同开发数据驱动分析，作为预测自然和人为系统中极端事件的途径，包括在陆地和太空天气、金融和经济以及网络安全方面的应用。该研讨会计划为期 3 天，以虚拟会议形式举行。它将强调来自代表性不足的社区的研究人员和学生的参与，这些社区可能特别容易受到极端事件的影响。实现更好的可预测性的一个重要步骤是不确定性量化，这本质上是一项跨学科的工作，需要跨多个学科的融合以及学术界、工业界和政府的多个利益相关者的参与。关于这一主题的融合加速器轨道将使多个部门受益。开发预测极端情况的框架需要利用各种来源的大量数据。研讨会将讨论为政府、行业和非营利组织的利益相关者开发一个共同平台的想法，作为融合加速器潜在新轨道的一部分。研讨会的主题因其对社会产生巨大影响的潜力而受到普遍关注。将制作一个研讨会记录，这将广泛展示该主题领域融合研究的潜在成果。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。