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.
InfoSymbioticSystems/InfoSymbiotics embody the power of the Dynamic Data Driven Applications Systems (DDDAS) paradigm, where data are dynamically integrated into an executing simulation to augment or complement the application model, and, where conversely the executing simulation steers the measurement (instrumentation and control) processes of the application system. In essence, the InfoSymbiotics/DDDAS control loop unifies complex computational models of a system with the real-time data-acquisition and control aspects of the system, and engenders transformative advances in computational modeling of applications and in instrumentation and control systems, and in particular those that represent dynamic, complex systems. Initial work on DDDAS has accomplished much towards demonstrating its potential and broad impact. The concept is recognized as key to important new capabilities, critical in many societal, commercial, and national and international priorities and initiatives, identified in important studies, blue ribbon-panels and other notable reports. The 2005 NSF Blue Ribbon Panel on Simulation Based Engineering Science characterized DDDAS as visionary and revolutionary concept. Recent scientific and technological roadmaps (NSF CyberInfrastructure Framework for the 21st Century (CIF21) and the Air Force Technology Horizons 2010 Report) highlight the need for advances requiring the integration of simulation, observation and actuation, as envisioned in the InfoSymbiotics/DDDAS concept. InfoSymbiotics/DDDAS has transitioned from being a concept to a new field, driving future research and technology directions towards new capabilities. This report outlines a research agenda, integrating the multidisciplinary research scope of DDDAS with opportunities motivated by roadmaps and recent technological advances, and transmits the research community’s call for systematic support of such a research agenda. A confluence of needs and recent technological advances render InfoSymbiotics/DDDAS approaches more opportune than ever. Systems of today and those foreseen in the future, be they natural, engineered or societal, will provide unprecedented opportunities for new capabilities, but with concomitant increased scales of complexity and interconnectivity. The ensuing "systems of systems", exhibit increased fragility where even small failures in a subset of any of the component systems have the potential of cascading effects across the entire set of systems. These new realities call for more advanced methods of systems analysis and management. The methods needed go beyond the static modeling and simulation methods of the past, to new methods, such as InfoSymbiotics/DDDAS which augment and enhance the system models through continually updated information from monitoring and control/feedback aspects of the system. Moreover, the need for autonomic capabilities and optimized management of dynamic and heterogeneous resources in complex systems makes ever more urgent the need for DDDAS approaches, not only at the design stage, but also for managing the operational cycle of such systems. Together with these driving needs of emerging systems, several technological and methodological advances over the last decade have produced added opportunities and impetus for DDDAS approaches. These include: multiscale/multi-modal modeling; ubiquitous sensoring and networks of large collections of heterogeneous sensors and actuators; increased networking capabilities for streaming large data volumes; multicore-based transformational computational capabilities at the high-end, and the real-time data acquisition and control systems. Capitalizing on the promise of the DDDAS concept and the successes of precedent initial research efforts, a multi-agency workshop, cosponsored by AFOSR and NSF, was convened on August 30-31, 2010, in Arlington VA, and attended by over 100 representatives from academia, industry and government, to address further opportunities that can be pursued and derived from InfoSymbiotics/DDDAS approaches and advances, and in the context of the changed landscape of underlying technologies and drivers referenced above. The scope of relevant efforts spans several dimensions, and requires multidisciplinary thinking and multidisciplinary research, for innovations in the entire hierarchy: from instrumentation for sensing and control, to the systems software, to the algorithms, to the applications built using them. The report identifies needs in each of these areas as well as critical science and technology challenges that must be met, and calls for synergistic research: in applications (for new methods where simulations are dynamically integrated with real-time data acquisition and control, and where application models are dynamically invoked); in algorithms (tolerant in their stability properties to perturbations from streamed data, and algorithmic methods for uncertainty quantification and for efficient estimation of error propagation across dynamically invoked application models; in systems software supporting applications that exhibit dynamic execution requirements (where models of the application are dynamically invoked, and where the application computational load, across the high-end platform and the sensors or controllers side, shifts across these platforms, during execution-time, depending on the DDDAS application’s dynamic requirements, and on resource availability); in instrumentation systems and "big-data" management (dynamic, adaptive, optimized management of instruments and heterogeneous collections of networks of sensors and/or networked controllers); and in cyberinfrastructures of unified computational and instrumentation platforms and their environments. These are not only opportunities for highly innovative research advances, but also opportunities that bridges academia and industry, inducing new and innovative directions in industry and developing a globally competitive workforce.
