Dynamic Data Driven Applications Systems

Dynamic Data Driven Applications Systems ("DDDAS") is a paradigm whereby the computation and instrumentation aspects of an application system are dynamically integrated with a feedback control loop, in the sense that instrumentation data can be dynamically incorporated into the executing model of the application (in targeted parts of the phase-space of the problem to either replace parts of the computation to speed-up the modeling or to make the model more accurate for aspects of the system not well represented by the model; this can be considered as the model "learning" from such dynamic data inputs), and in reverse the executing model can control the system's instrumentation to cognizantly and adaptively acquire additional data (or search through archival data), which in-turn can improve or speedup the model (modeling process). DDDAS-based approaches have been shown that they can enable more accurate and faster modeling and analysis of the characteristics and behaviors of a system and can exploit data in intelligent ways to convert them to new capabilities, including decision support systems with the accuracy of full-scale modeling, executing model-driven adaptive management of complex instrumentation (including adaptive coordination across multitudes of heterogeneous sensors and controllers), as well as efficient data collection, management, and data mining.

The power of the DDDAS paradigm is that it involves a dynamically adapting and system-cognizant model (for example a model cognizant of the physics of the system, or other inherent characteristics and representations of the system), which "learns" and adapts through the "dynamic data" inputs at execution time, can discern false data and avoids the pitfalls of traditional Machine Learning approaches which can go rogue. Moreover, unlike ML methods, DDDAS enables more accurate and faster modeling and analysis, for "systems analytics" rather than simply "data analytics", and the DDDAS computational and instrumentation frameworks, include in addition to comprehensive system-characteristics cognizant representations and models, software and hardware (computational and instrumentation) platforms architectures and services, and can also include the human-in-the-loop, as complex systems typically involve.

DDDAS-based approaches have demonstrated new capabilities in systems modeling and instrumentation, as well as autonomic capabilities in many areas, ranging from fundamental studies in materials properties (e.g., nanomaterials), to structural and civil engineering (e.g., smart buildings) and aerospace, to manufacturing (process planning and control; additive manufacturing), transportation systems, energy systems (e.g., smart power-grids), environmental (e.g., wildfires), weather (atmospheric and space), medical diagnosis and treatment, cloud computing, IoT, and communications systems, cybersecurity, and more.The DDDAS site contains links on the extensive work and impact of the DDDAS paradigm.