AFOSR/NSF Co-Sponsored Workshop on

All WGs should address the following common and overarching issues:

• The scope of research challenges is clearly wide and in need of fundamental advances. Why is now the right time for fostering this kind of research?
• What are the Grand S&T Challenges in enabling DDDAS? What are ongoing research advances can be used as leverage and springboard to enable DDDAS?

• What kinds of processes, venues and mechanisms are optimal to facilitate the multidisciplinary nature of the research needed in enabling such capabilities?
• What past or existing initiatives can contribute, and what new ones should be created to systematically support such efforts?
• What are the benefits of coordination and joint efforts across agencies, nationally and in supporting synergistically such efforts?
• What kinds of connections with the industrial sector can be beneficial? How can these be fostered effectively to focus research efforts and expedite technology transfer?
• How these new research directions can be used to create exciting new opportunities for undergraduate, graduate and postdoctoral education and training?
• What novel and competitive workforce development opportunities can ensue?
• What National and International critical challenges are addressed through DDDAS capabilities?

DDDAS environments require algorithms, mathematical and statistical, both numeric and non-numeric, that have good convergence properties under perturbations from streamed data into the executing application. DDDAS goes beyond the traditional data-assimilation approaches:

• What is the state-of-the-art and what are the challenges in the applications algorithms to enable such capabilities for the applications models/simulations?
• What algorithms’ development is needed to enable application algorithms tolerant to perturbations from "on-line" input data, and with good stability properties?
• How can one select and incorporate dynamically appropriate algorithms as the application requirements and data sets change in the course of the simulation?
• What kinds of approaches, such as knowledge-based systems, can be employed, and what interfaces and applications assists are needed to allow such capabilities?
• What systems support is required to develop such environments?
• How do the existing methods and capabilities in the above need to be advanced?

DDDAS environments entail application models that can interface and dynamically interact with the measurement data systems (archival, real-time data acquisition and control systems). Such interaction entails dynamic application models and application components, at runtime, as dictated by the streamed data, and can include dynamic invocation of models at multiple scales – that is "dynamic multi-scale". Models, experiments and observations are all representations and discrete samples of behavior. Quantifying and managing the outcomes of application systems (predictions, control actions, …) must account for these uncertainties. Such situations ensue new and increased challenges, beyond the traditional multi-scale, and uncertainty quantification considerations.

DDDAS inherently involves large amounts of data that can result from heterogeneous and distributed sources, collected in differing time-scales and in different formats, and which need to be preprocessed before automatically integrating them to the executing applications that need use the new data.

• What is the state of the art in measurement systems and how are they integrated in DDDAS, where measurements from sensors, other instruments and data repositories are dynamically integrated with the application modeling to improve the application modeling?
• Conversely, what is the state of the art in on-line application control of the measurement instrument or process providing opportunity to improve the measurement process, guide the design and operational aspects of measurement instruments, and networks of distributed heterogeneous sensors and networks of embedded controllers?
• What are the methods that need to be developed to guide the architecture of sets of sensors and other instruments thus improving the effectiveness or efficiency of the measuring systems, and networks of distributed heterogeneous sensors and networks of embedded controllers?
• What are the challenges and opportunities in software and hardware technologies to enable such dynamic interfaces to such measurement and control systems, and their associated data sets? What improvements in the methods are expected, how are they going to be enabled?
• How the existing methods and capabilities in all the above need to be advanced?

DDDAS integrates real-time sensor and other measurement devices with special purpose data processing systems together with the parts of the application that execute in larger platforms and driving a seamless integration of stationary and mobile devices together with large high-end platforms, entailing grids that go beyond the present computational grids.

• What are the challenges in the infrastructure just described above?
• What are the challenges and opportunities in software and hardware technologies to enable such dynamic interfaces?
• What improvements in the measurement methods are expected and how are they going to be enabled?

Quality of service, program software environments, data massaging, network security, and availability of common libraries are all important to making a DDDAS work in a global manner.

• What is the state-of-the-art and what advances are needed in algorithms and software and what new capabilities need to be provided by the underlying systems and platforms on which these applications execute, so that quality of service is ensured?
• What are the software challenges in the programming environments for the development and runtime support, under conditions where the underlying resources as well as the applications requirements might be changing at execution time?
• What are the issues in data management, dynamic selection of components, dynamic invocation of components, mapping to underlying resources, interfaces for request, and allocation of systems resources so that quality of service is ensured for the applications?
• What are the additional capabilities that are needed in the application support and systems management services?
• How can these be fostered effectively to focus research efforts and expedite technology transfer?