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What is DDDAS? DDDAS stands for "Dynamic Data-Driven Application System". DDDAS is a paradigm whereby application (or simulations) and measurements become a symbiotic feedback control system. DDDAS 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 process. Such capabilities promise more accurate analysis and prediction, more precise controls, and more reliable outcomes. The ability of an application to control and guide the measurement process and determine when, where, and how it is best to gather additional data has itself the potential of enabling more effective measurement methodologies. Furthermore, the incorporation of dynamic inputs into an executing application invokes new system modalities and helps create application software systems that can more accurately describe real world, complex systems. This enables the development of applications that intelligently adapt to evolving conditions and that infer new knowledge in ways that are not predetermined by the initialization parameters and initial static data. The need for such dynamic applications is already emerging in business, engineering and scientific processes, analysis, and design. Manufacturing process controls, resource management, weather and climate prediction, traffic management, systems engineering, civil engineering, geological exploration, social and behavioral modeling, cognitive measurement, and bio-sensing are examples of areas likely to benefit from DDDAS. DDDAS creates a rich set of new challenges for applications, algorithms, systems’ software, and measurement methods. DDDAS research typically requires strong, systematic collaborations between applications domain researchers and mathematics, statistics, and computer sciences researchers, as well as researchers involved in the design and implementation of measurement methods and instruments. Consequently, most DDDAS projects involve multidisciplinary teams of researchers. Dynamic Data Driven Integrated Simulation and Stochastic Optimization for Wildland Fire Containment" PI: L. Ntaimo, Co-PI: X. Hu, Dept. of Computer Science, Georgia State University Sponsor: National Science Foundation Grant No. grant CNS 0540000 Duration: Dec 05 - Nov 08. PROJECT ABSTRACT The purpose of the proposed research is to develop a dynamic data driven real-time decision support system for wildland fire spread prediction and containment that integrates simulation and stochastic optimization. The computational aspects of the decision support system include wildland fire spread simulation using the discrete event system specification (DEVS) approach and decision-making under uncertainty concerning where and when to concentrate fire containment efforts using stochastic programming. The experimental measurement aspects of the application include real-time dynamic weather conditions and sensory feedback data that include the actual fire-front position and the effect of fire suppression efforts on fire propagation. The success of the proposed research will derive from the multidisciplinary team of investigators whose areas of research include stochastic programming, discrete event modeling and simulation, systems software, and wildland fire spread.
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