Abstract. Dynamic Data Driven Application Systems (DDDAS) entails the ability to incorporate additional data into an executing application -these data can be archival or collected on-line; and in reverse, the ability of applications to dynamically steer the measurement process. The paradigm offers the promise of improving modeling methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. This presents the potential to transform the way science and engineering are done, and induce a major impact in the way many functions in our society are conducted, such as manufacturing, commerce, hazard management, medicine. Enabling this synergistic feedback and control-loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms tolerant to perturbations from dynamic data injection and steering, and systems software to support the dynamic environments of concern here. Recent advances in complex applications, the advent of grid computing and of sensor systems, are some of the technologies that make it timely to embark in developing DDDAS capabilities. Research and development of such technologies requires synergistic multidisciplinary collaboration in the applications, algorithms, software systems, and measurements systems areas, and involving researchers in basic sciences, engineering, and computer sciences. The rest of the papers in the proceedings of this workshop provide examples of ongoing research developing DDDAS technologies within the context of specific and important application areas.
For example, this approach has been receiving increased attention recently in the context of dynamic data driven application systems (DDDAS) REF .
Dynamic Data Driven Applications Systems : A New Paradigm for Application Simulations and Measurements
International Conference on Computational Science
Computer Science