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

动态数据驱动应用系统(DDDAS)需要将附加数据动态合并到正在执行的应用程序中的能力，反过来，也需要应用程序动态引导测量过程的能力。在DDDAS中，测量一词被用来暗指仪器系统、异类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、新颖和强大的技术，用于改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能力的应用。DDDAS创造了改变科学和工程工作方式的潜力，在过程的建模、设计、实施、分析和理解的方式以及我们社会中许多功能在众多广泛领域的执行方式方面产生了根本性和重大的进步，例如制造、商业、运输、危险预测/管理和医学。从以高昂成本部署的各种传感器获得前所未有的数据需要开发在建模和模拟中利用这些数据的方法。从冰盖到湖泊水质和污染物运输，越来越多的科学家被要求基于对模型和观测数据的最佳使用来提供预测？因此，这类活动的方法和理论需要成为研究投资的一个非常重要的优先事项。在数据可获得性、不确定性量化方法和数据同化方法方面的最新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学者、国家实验室人员和资助机构代表，以制定目标、优先事项和计划，以实现上述宏伟愿景。

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.