Four attributes of DDDAS include (1) instrumentation methods, (2) real- world applications, (3) modeling and simulation, and (4) systems software.

This website is dedicated to showcasing scientific and technological advances in complex systems modeling and instrumentation methods enabled under the rubric of the Dynamic Data-Driven Applications System paradigm. The website is a scientific community forum with hyperlinks to DDDAS research projects, virtual proceedings, related software, announcements and other news.

As articulated by Dr. Darema who pioneered the DDDAS paradigm: “in DDDAS instrumentation data and executing application models of these systems become a dynamic feedback control loop, whereby measurement data are dynamically incorporated into an executing model of the system in order to improve the accuracy of the model (or simulation), or to speed-up the simulation, and in reverse the executing application model controls the instrumentation process to guide the measurement process. DDDAS presents opportunity to create new capabilities through more accurate understanding, analysis, and prediction of the behavior of complex systems, be they natural, engineered, or societal, and to create decision support methods which can have the accuracy of full-scale simulations, as well as to create more efficient and effective instrumentation methods, such as intelligent management of Big Data, and dynamic and adaptive management of networked collections of heterogeneous sensors and controllers. DDDAS is a unifying paradigm, unifying the computational and instrumentation aspects of an application system, extends the notion of Big Computing to span from the high-end to the real-time data acquisition and control, and it’s a key methodology in managing and intelligently exploiting Big Data”

The DDDAS paradigm, and opportunities and challenges in exploiting the DDDAS paradigm have been discussed in a series of workshops, starting in 2000. The reports from these workshops, included in the present website, identified new science and technology capabilities, driven by and enabled through the DDDAS paradigm, and these include new modeling methods, algorithms, systems software, and instrumentation methods, and well as the need for synergistic multidisciplinary research among application domains researchers, and researchers in mathematics, statistics, and computer sciences, as well as well as researchers involved in design and development of instrumentation systems and methods. In addition to these workshops, through corresponding government sponsored initiatives, research efforts commenced to address the challenges and create new capabilities. As shown through the increasing body of work, DDDAS is applicable to many areas, such as civil engineering, aerospace, manufacturing, transportation, energy, medical diagnosis and treatment, environmental, weather, and climate, etc. This website presents examples of advances through DDDAS in areas and aspects identified above

Introduction to DDDAS

Conference and Workshop News.

AFOSR Solicitation 

AFOSR BAAs are available here by typing the organization name (e.g. "AFOSR") into the keyword field or using CFDA numbers 12.800, 12.630 and 12.910.

(Dynamic Data Driven Applications Systems), is a new paradigm whereby  the computation and instrumentation aspects of an application system are dynamically integrated in a feed-back control loop, in the sense that instrumentation data can be dynamically incorporated in to the executing model of the application, and in reverse the executing model can control the instrumentation.  Such approaches have been shown that 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, efficient data collection, management, and data mining.

DDDAS Challenges and Processes

The key developments of the integration of the instrumentation, models, and software to enable the development of DDDAS include theory, algorithms, and computation. The theory includes mathematical advances (retrospective cost modeling - check); while the algorithms support new paradigms (e.g., ensemble Kalman filter, Particle filter, optimization techniques). The computation methods align with the developments in the continuing networked society such as non-convex optimization and data flow architectures.

The  challenges  DDDAS  seeks  to  advance  include  data  modeling,  context processing,  and  content  application.  To  bring  together  data,  context  and  content requires addressing issues in model fidelity such as how many parameters are needed for system control. When data is collected, it needs to be preprocessed to determine whether its inherent information matches the context. One example includes clutter reduction,  sensor  registration,  and  confuser  analysis  in  vehicle  tracking.  Finally, another key challenge is that of sampling. Sampling is the multiresolution needed to monitor the situation, environment and network context to explain the content desired.