RTG: Program in Computation- and Data-Enabled Science

RTG：计算和数据支持科学项目

• 批准号: 2136228
• 负责人: Jay Gopalakrishnan
• 金额: $ 213.54万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136228&HistoricalAwards=false
• 关键词: RTG; Program; Computation; Data; Enabled

This Research Training Group (RTG) in Computation- and Data-Enabled Science (CADES) at Portland State University is designed to train students and postdocs in computational mathematics and statistics, as well as enable them to develop a broad understanding of current issues in data-driven science. Targeted research directions of societal impact include simulation of optical fibers that drive today's internet, forecasting of weather, air quality, and drought, understanding progression of diseases such as cancer and dementia, and optimizing warehouse locations and wireless services. The research, at the intersection of mathematics, statistics, and computing, is characterized by intellectual diversity of techniques. Integration across these disciplines is expected to result in enhanced research productivity and uniquely qualified trainees. For this RTG, eight faculty experts integrate research and training with service for the city and the local community. The research group effort integrates numerical techniques for partial differential operators, data-intensive statistical learning, and optimization methods for data science. Specific projects include simulation of light propagation in microstructured optical devices using advanced eigensolvers, improvements to time-evolving simulations by spacetime approaches with and without causality, learning dynamical systems from noisy data, kernel methods for randomized control trials, advanced data assimilation for prediction of complex systems, and optimization methods for multifacility location and machine learning. Mechanisms to accelerate the entry of trainees into these research topics are integrated into the program. The project will establish a Consulting Lab for client-based research and training experiences using real-world data, a byproduct of which is the creation of new consulting services for regional clients. Training innovations include a new seminar favoring dialogue over monologue, buy-in from leaders in the field as external examiners, summer boot camps to overcome anticipated lack of trainee prerequisites for transdisciplinary crossovers, identification of selected external partnering institutions for topical internships, and city-based and community-serving research experiences for undergraduates. All projects require high performance computing and open-source scientific software products. Training in these topics are valued byproducts. The RTG recruitment activities are targeted to increase participation of underrepresented groups. Engagement from external partners in need of US citizen employees augments job prospects for the trainees and helps address shortages in the technical workforce.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.

波特兰州立大学计算和数据科学 (CADES) 的研究培训小组 (RTG) 旨在培训计算数学和统计学方面的学生和博士后，并使他们能够对当前数据问题有广泛的了解驱动的科学。具有社会影响力的有针对性的研究方向包括模拟驱动当今互联网的光纤、预测天气、空气质量和干旱、了解癌症和痴呆症等疾病的进展以及优化仓库位置和无线服务。这项研究涉及数学、统计学和计算的交叉领域，其特点是技术知识的多样性。这些学科的整合预计将提高研究生产力并培养独特的合格学员。在这个 RTG 中，八名教师专家将研究和培训与为城市和当地社区提供的服务结合起来。该研究小组的工作整合了偏微分算子的数值技术、数据密集型统计学习和数据科学的优化方法。具体项目包括使用先进的本征解算器模拟微结构光学器件中的光传播、通过有因果关系和无因果关系的时空方法改进时间演化模拟、从噪声数据中学习动力系统、随机控制试验的内核方法、用于预测复杂情况的高级数据同化系统以及多设施定位和机器学习的优化方法。该计划纳入了加速学员进入这些研究主题的机制。该项目将建立一个咨询实验室，利用真实数据进行基于客户的研究和培训体验，其副产品是为区域客户创建新的咨询服务。培训创新包括一个新的研讨会，有利于对话而不是独白，从该领域的领导者那里获得作为外部审查员的支持，夏季训练营以克服预期缺乏跨学科交叉的受训者先决条件，确定选定的外部合作机构进行专题实习，以及城市-为本科生提供基于社区的研究经验。所有项目都需要高性能计算和开源科学软件产品。这些主题的培训是有价值的副产品。 RTG 招募活动旨在提高代表性不足群体的参与度。需要美国公民雇员的外部合作伙伴的参与增加了受训人员的就业前景，并有助于解决技术劳动力的短缺问题。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势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

Relationships Between Polyhedral Convex Sets and Generalized Polyhedral Convex Sets

多面体凸集与广义多面体凸集之间的关系

• DOI: 10.1007/s10957-023-02269-2
• 发表时间: 2022-12-23
• 期刊: Journal of Optimization Theory and Applications
• 影响因子: 1.9
• 作者: Nguyen Ngoc Luan;N. M. Nam;N. N. Thieu;N. D. Yen
• 通讯作者: N. D. Yen

Estimating Ocean Observation Impacts on Coupled Atmosphere‐Ocean Models Using Ensemble Forecast Sensitivity to Observation (EFSO)

估计海洋观测对耦合大气的影响 - 使用集合预报观测灵敏度 (EFSO) 的海洋模型

• DOI: 10.1029/2023gl103154
• 发表时间: 2023-10
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Chang, Chu‐Chun;Chen, Tse‐Chun;Kalnay, Eugenia;Da, Cheng;Mote, Safa
• 通讯作者: Mote, Safa

Generalized optimal transport and mean field control problems for reaction-diffusion systems with high-order finite element computation

高阶有限元计算反应扩散系统的广义最优输运和平均场控制问题

• DOI: 10.1016/j.jcp.2024.112994
• 发表时间: 2024-07
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Fu, Guosheng;Osher, Stanley;Pazner, Will;Li, Wuchen
• 通讯作者: Li, Wuchen

Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

使用数据驱动的振荡模式预测改进南亚季风降雨的次季节预测

• DOI: 10.1073/pnas.2312573121
• 发表时间: 2024-04
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Bach, Eviatar;Krishnamurthy, V.;Mote, Safa;Shukla, Jagadish;Sharma, A. Surjalal;Kalnay, Eugenia;Ghil, Michael
• 通讯作者: Ghil, Michael

Evaluation of Inner Products of Implicitly Defined Finite Element Functions on Multiply Connected Planar Mesh Cells

多重连通平面网格单元上隐式定义有限元函数内积的计算

• DOI: 10.1137/23m1569332
• 发表时间: 2024-02
• 期刊: SIAM Journal on Scientific Computing
• 影响因子: 3.1
• 作者: Ovall, Jeffrey S.;Reynolds, Samuel E.
• 通讯作者: Reynolds, Samuel E.