DDDAS is a paradigm whereby instrumentation data are dynamically integrated into an executing application model while in reverse the executing model controls the instrumentation. The scope of application areas ranges from the nano-scale to the extra-terra-scale. For example, it includes applications in (but not limited to) aerospace, critical infrastructures, biological sciences and geosciences, cyber security, and resilient architectures.

In the DDDAS paradigm, data inputs into dynamically determined parts of the model phase-space increase modeling speed and accuracy for enhanced analysis and prediction of system evolution and behavior and create decision support systems with the accuracy of full-scale models. Dynamic control of instrumentation enables efficient operation of large instruments and adaptive management of distributed and heterogeneous collections of sensor and controller resources.  The DDDAS paradigm also implies dynamic integration across the range of computational platforms – from the high-end to the real-time. It synergistically exploits computing at the high-end as well as the tremendous computational power of collections of sensors and controllers (Large-Scale-Big-Computing) and the second wave of “Big Data” that they entail (Large-Scale-Big-Data).

Developing DDDAS capabilities entails multidisciplinary collaborative research and advances in fundamental areas such as stochastic systems, modeling, simulation, sensing, inference, planning, control, decision support, learning, optimization and cyber infrastructures.  The DDDAS community has made significant progress in closing the loop between Data and Knowledge, through improved modeling processes, understanding and mitigating model error with the aid of instrumentation, and controlling the instrumentation to turn the Big Data deluge into smart data regimes.

Participants from academia, industry, government and international counterparts will report original work where DDDAS research is advancing scientific frontiers, engendering new engineering capabilities, and adaptively optimizing operational processes.  The InfoSymbiotics/DDDAS conference spans a broad set of topics and interests.

We invite you to submit an extended abstract, which will be reviewed and notified of its acceptance as a paper or a poster. Select papers appearing in the conference will be published in a special issue of Springer.

Sai Ravela and Erik Blasch
{sairavela@gmail.com; erik.blasch@gmail.com}