Workshop on Dynamic Data-Driven Applications Systems (DDDAS) - InfoSymbiotic Systems

动态数据驱动应用系统 (DDDAS) 研讨会 - InfoSymbiotic Systems

• 批准号: 1057753
• 负责人: Craig Douglas
• 金额: $ 4万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1057753&HistoricalAwards=false
• 关键词: Workshop; Dynamic; Data; Driven; Applications

Dynamic Data Driven Application Systems (DDDAS) entails the ability to dynamically incorporate additional data into an executing application, and, conversely, the ability of an application to dynamically steer the measurement process. In DDDAS, the term measurement has been used to connote instrumentation systems, networks of heterogeneous sensors, and embedded controllers. This synergistic and symbiotic feedback control-loop between applications and measurements is a high payoff, novel, and powerful technique for advancing modeling and simulation methods plus instrumentation and control methods, thus creating applications with new and enhanced capabilities. DDDAS creates the potential to transform the way science and engineering are done, to induce fundamental and major advances in the way processes are modeled, designed, implemented, analyzed and understood, and in the way many functions in our society are conducted in numerous broad arenas, e.g., manufacturing, commerce, transportation, hazard prediction/management, and medicine.The unprecedented availability of data from a variety of sensors deployed at great cost requires the development of methodologies for exploiting this data in modeling and simulation. From ice sheets to lake water quality and contaminant transport scientists are increasingly called upon to deliver predictions based on the optimal use of modeling and observation data ? methodologies and theory for such activities thus needs to be a very high priority for research investment. Recent developments in terms of data availability, methodologies for uncertainty quantification and data assimilation have made possible transformative new research. The workshop proposed here will bring together an eclectic group of academics, national laboratory personnel, and funding agency representatives to formulate objectives, priorities, and plans to enable the grand vision described above.

动态数据驱动应用系统（DDDAS）需要将附加数据动态地合并到正在执行的应用中的能力，并且相反地，需要应用动态地引导测量过程的能力。在DDDAS中，术语测量已被用于暗示仪器系统、异构传感器网络和嵌入式控制器。这种应用和测量之间的协同和共生反馈控制回路是一种高回报、新颖且强大的技术，用于推进建模和仿真方法以及仪器和控制方法，从而创建具有新的和增强的功能的应用。DDDAS创造了改变科学和工程方式的潜力，在建模，设计，实施，分析和理解过程的方式方面带来了根本性和重大的进步，并且在我们社会的许多功能中进行了许多广泛的领域，例如，来自以高成本部署的各种传感器的数据的前所未有的可用性要求开发用于在建模和仿真中利用这些数据的方法。从冰盖到湖泊水质和污染物传输，科学家们越来越多地被要求提供基于最佳使用模型和观测数据的预测。因此，这些活动的方法和理论需要成为研究投资的高度优先事项。最近在数据可用性、不确定性量化方法和数据同化方面的发展使变革性的新研究成为可能。这里提议的研讨会将汇集一个由学者、国家实验室人员和资助机构代表组成的折衷小组，制定目标、优先事项和计划，以实现上述宏伟愿景。