|Frederica Darema||Mario Rotea|
|Marvin Goldberg||Daniel H. Newlon|
|John C. Cherniavsky||EHR/DRL||Juan E. Figueroa|
|Jeanne E. Hudson||Doris A. Hutchinson|
Important Information for Proposers
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 17-1), is effective for proposals submitted, or due, on or after January 30, 2017. Please be advised that, depending on the specified due date, the guidelines contained in NSF 17-1 may apply to proposals submitted in response to this funding opportunity.
Information technology-enabled applications/simulations of systems in science and engineering have become as essential to advances in these fields as theory and measurement. This triad of approaches is used by scientists and engineers to analyze the characteristics and predict the behavior of complex systems and the applications that represent them. However, accurate and comprehensive analysis and prediction of the behavior of complex systems over time is difficult. With traditional simulation and measurement approaches, even elaborate computational models of such systems produce applications and simulations that diverge from or fail to predict real system behaviors.
This solicitation focuses explicitly on Dynamic Data Driven Applications Systems (DDDAS), a promising concept in which the computational and experimental measurement aspects of a computing application are dynamically integrated, creating new capabilities in a wide range of science and engineering application areas. Computational aspects of DDDAS may be realized on a diverse set of computer platforms including computational grids, leadership-class supercomputers, mid-range clusters, distributed, high-throughput computing environments, high-end workstations, and sensor networks. Consequently, DDDAS-funded projects are expected to make significant contributions to research advances in computational science and engineering, high-end computing, measurement methods, and cyberinfrastructure.
DDDAS is a paradigm whereby application/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/simulation 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 adapt intelligently to evolving conditions, and that infer new knowledge in ways that are not predetermined by startup parameters. 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, geo-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. The research scope described here 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 projects proposed in response to this solicitation are expected to involve teams of researchers. Following merit review of the proposals received, projects will be selected for support by NSF, the National Institutes of Health (NIH) and the National Oceanic and Atmospheric Administration (NOAA).
More Information on DDDAS