NRT-DESE: Preparing Resilient and Operationally Adaptive Communities through an Interdisciplinary, Venture-based Education (PROACTIVE)

这项授予克莱姆森大学的国家科学基金会研究培训(NRT)奖将回应对能够跨越学科界限评估技术和社会风险、将这些风险传达给决策者并制定战略以提高社区对自然或人为灾难的适应能力的迫切需求。在过去的50年里，自然灾害的频率和影响急剧增加，人为灾害的风险显著增加，特别是自9/11事件以来。由于基础设施系统之间存在复杂的、了解不足的反馈回路，预测和减轻此类极端事件很困难。社会需要专业人员，他们能够将物理、网络和人类基础设施系统融合在一起的复杂系统概念化，并能够将这种概念性理解转化为可靠的计算模型，并通过数据进行验证。此外，这些专业人员必须具备与其他学科的同行以及与决策和政策制定者有效沟通的技能，以确保科学与政策之间的凝聚力。该NRT奖将通过课程开发、研究生教育转型和具有社会影响的研究来应对这些挑战。该项目预计将培训五十二(52)名MS和博士生，其中包括二十六(26)名资助的受训人员，他们来自与基于模型和数据的基础设施恢复能力相关的各种科学和工程学科。该项目设想了一种新的研究生教育模式，有助于培养STEM专业人员，他们是跨学科的系统思考者，能够跨越学科界限，在不断学习个人和不断发展的知识的动态网络中工作。它代表着研究生教育的变革，通过创建具有强大同行学习方面的协作研究社区，导致当地的科学社区，使学生能够学习商业？科学(网络、协作、交流等)。NRT奖将通过开发模块化、个性化的培训计划，促进灵活、适应性强的课程结构，以应对学生不断变化的需求。它会增强学生的能力吗？能够通过一个独特的集成研究、培训和推广计划，将学术研究应用于复杂的现实世界问题，并意识到社会影响，该计划研究对低收入地区造成不成比例影响的基础设施脆弱性。该培训计划在一个逻辑框架内开发，并有一个全面的、研究驱动的评估计划，将可在更大范围内复制。学生和教师团队将在三个核心的模型工程和数据科学领域进行研究：(1)将模型整合到模型中，(2)将数据整合到模型中，以及(3)将模型预测传达给决策者。他们在每个领域的工作将使他们能够突出关键的模型/数据科学问题，了解这些问题如何转化为基础设施系统中的漏洞造成的社会影响，并开发解决方案以减轻潜在基础设施漏洞造成的损害。对基础设施弹性的研究将产生建模和分析耦合系统的新方法，使科学家和决策者能够聚集在一起，更好地了解相互依赖的基础设施系统及其不确定性。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、具有潜在变革性的新模式。培训路径致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合培训模式，在高度优先的跨学科研究领域对STEM研究生进行有效培训。

This National Science Foundation Research Traineeship (NRT) award to Clemson University will respond to the urgent need for professionals capable of crossing disciplinary boundaries to assess technological and societal risks, to communicate those risks to decision makers, and to devise strategies that improve community resilience to natural or man-made disasters. The last five decades have seen a sharp increase in the frequency and impact of natural hazards and a significantly higher risk of man-made disasters, particularly since 9/11. Predicting and mitigating such extreme events is difficult due to interactions among infrastructure systems with complex, poorly understood feedback loops. Society needs professionals who can conceptualize complex systems where physical, cyber, and human infrastructure systems converge and who can transform this conceptual understanding into reliable computational models that are validated by data. Moreover, these professionals must be equipped with skills to effectively communicate with their peers in other disciplines and with decision and policy makers to ensure cohesion between science and policy. This NRT award will address these challenges through curriculum development, transformation in graduate education, and research with societal impact. The project anticipates training fifty-two (52) MS and PhD students, including twenty-six (26) funded trainees, from a variety of science and engineering disciplines related to model- and data-enabled infrastructure resiliency. This project envisions a new paradigm of graduate education conducive to training STEM professionals who are transdisciplinary system thinkers capable of crossing disciplinary boundaries and working in a dynamic network of continuously learning individuals and evolving knowledge. It represents a transformation in graduate education through the creation of collaborative research communities with strong peer-learning aspects, resulting in a local scientific community that enables students to learn the ?business? of science (networking, collaboration, communication, etc.). The NRT award will promote an agile, adaptive curriculum structure responsive to the changing needs of students through the development of a modular, personalized training program. It will enhance students? ability to apply academic research to complex, real-world problems with an awareness of societal impacts via a uniquely integrated research, training, and outreach program that studies infrastructure vulnerabilities that disproportionately affect low-income regions. Developed within a logic framework and with a thorough, research-driven evaluation plan, this training program will be reproducible on a larger scale. Student and faculty teams will conduct research in three core model engineering and data science areas: (1) integrating models to models, (2) incorporating data into models, and (3) communicating model predictions to decision makers. Their work in each of these areas will allow them to highlight key model/data science issues, understand how these issues translate to societal impacts caused by vulnerabilities in infrastructure systems, and develop solutions to mitigate damage caused by potential infrastructure vulnerabilities. The research on infrastructure resiliency will result in new approaches for modeling and analyzing coupled systems, enabling scientists and decision makers to come together to better understand interdependent infrastructure systems and their uncertainties.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.