DDDAS provides new approaches for combining computational, theoretical, and instrumentation data sets for high interactive testing of multiple physical and engineering hypotheses.
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.
The DDDAS concept entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement (instrumentation and control) components of the application system. DDDAS is a key concept for improving modeling of systems under dynamic conditions, more effective management of instrumentation systems, and is a key concept in architecting and controlling dynamic and heterogeneous resources, including, sensor networks, networks of embedded controllers, and other networked resources. DDDAS transformative advances in computational modeling of applications and in instrumentation and control systems (and in particular those that represent dynamic systems) require multidisciplinary research, and specifically need synergistic and systematic collaborations between applications domain researchers with researchers in mathematics and statistics, researchers computer sciences, and researchers involved in the design/ implementation of measurement and control systems (instruments, and instrumentation methods, and other sensors and embedded controllers).
Individual and multidisciplinary research, technology development, and cyber Infrastructure software frameworks needed for DDDAS applications and their environments are sought, along four key science and technology frontiers:  Applications modeling: In DDDAS an application/simulation must be able to accept data at execution time and be dynamically steered by such dynamic data inputs. This requires research advances in application models that: describe the application system at different levels of detail and modalities; are able to dynamically invoke appropriate models as needed by the dynamically injected data into the application; and include interfaces of applications to measurements and other data systems. DDDAS will, for example, engender an integration of large scale simulation with traditional controls systems methods, thus provide an impetus of new directions to traditional controls methods.  Advances in Mathematical and Statistical Algorithms include creating algorithms with stable and robust convergence properties under perturbations induced by dynamic data inputs: algorithmic stability under dynamic data injection/streaming; algorithmic tolerance to data perturbations; multiple scales and model reduction; enhanced asynchronous algorithms with stable convergence properties; multimodal, multiscale modeling and uncertainty quantification, and in cases where the multiple scales or modalities are invoked dynamically and there is need for fast methods of uncertainty quantification and uncertainty propagation across dynamically invoked models. Such aspects push to new levels of challenges the traditional computational math approaches.  Application Measurement Systems and Methods include improvements and innovations in instrumentation platforms, and improvements in the means and methods for collecting data, focusing in a region of relevant measurements, controlling sampling rates, multiplexing, multisource information fusion, and determining the architecture of heterogeneous and distributed sensor networks and/or networks of embedded controllers. The advances here will create new instrumentation and control capabilities. Advances in Systems Software runtime support and infrastructures to support the execution of applications whose computational systems resource requirements are dynamically dependent on dynamic data inputs, and include: dynamic selection at runtime of application components embodying algorithms suitable for the kinds of solution approaches depending on the streamed data, and depending on the underlying resources, dynamic workflow driven systems, coupling domain specific workflow for interoperation with computational software, general execution workflow, software engineering techniques. The systems software environments required are those that can support execution in dynamically integrated platforms ranging from the high-end to the real-time data acquisition and control – cross-systems integrated.  Software Infrastructures and other systems software (OS, data-management systems and other middleware) services to address the “real time” coupling of data and computations across a wide area heterogeneous dynamic resources and associated adaptations while ensuring application correctness and consistency, and satisfying time and policy constraints. Specific features include the ability to process large volume, high rate data from different sources including sensor systems, archives, other computations, instruments, etc.; interfaces to physical devices (including sensor systems and actuators), and dynamic data management requirements
Areas of interest to the AF and which can benefit from DDDAS transformative advances, include areas driven by the AF Technology Horizons, Energy Horizons, and Global Horizons reports, such as: autonomous systems (e.g. swarms of unmanned or remotely piloted vehicles); autonomous mission planning; complex adaptive systems with resilient autonomy; collaborative/cooperative control; autonomous reasoning and learning; sensor-based processing; ad-hoc, agile networks; multi-scale simulation technologies and coupled multi-physics simulations; decision support systems with the accuracy of full scale models (e.g. high-performance aircraft health monitoring, materials stresses and degradation); embedded diagnostics and V&V for complex adaptive systems; automated software generation; cognitive modeling; cognitive performance augmentation; human-machine interfaces. DDDAS provides new approaches for combining computational, theoretical, and instrumentation data sets for high interactive testing of multiple physical and engineering hypotheses.
DDDAS: ability to dynamically incorporate additional data into an executing application, and in reverse, ability of an application to dynamically steer the measurement process.