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

动态数据驱动的应用程序系统（DDDAS）需要动态地将附加数据合并到正在执行的应用程序中的能力，反过来，应用程序也需要动态地引导度量过程的能力。在DDDAS中，测量一词被用来指代仪器系统、异构传感器网络和嵌入式控制器。这种应用程序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功能的应用程序。DDDAS创造了改变科学和工程完成方式的潜力，在过程建模、设计、实施、分析和理解的方式上，以及在我们社会中许多功能在许多广泛领域（如制造业、商业、运输、危害预测/管理和医学）的执行方式上，诱导了根本性和重大的进步。以巨大成本部署的各种传感器所提供的数据的空前可用性要求开发在建模和仿真中利用这些数据的方法。从冰盖到湖水质量和污染物运输，越来越多的科学家被要求根据建模和观测数据的最佳利用来提供预测。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。在数据可用性、不确定性量化方法和数据同化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一群折衷的学者、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.