This website is dedicated to showcasing scientific and technological advances in complex systems modeling and instrumentation methods enabled under the rubric of the Dynamic Data-Driven Applications System paradigm. The website is a scientific community forum with hyperlinks to DDDAS research projects, virtual proceedings, related software, announcements and other news.

Collabporation Opportunities

  1. SFFP –
  2. FrontDoor –
  3. AFWERX –

DDDAS Challenges and Processes

The key developments of the integration of the instrumentation, models, and software to enable the development of DDDAS include theory, algorithms, and computation. The theory includes mathematical advances (retrospective cost modeling – check); while the algorithms support new paradigms (e.g., ensemble Kalman filter, Particle filter, optimization techniques). The computation methods align with the developments in the continuing networked society such as non-convex optimization and data flow architectures.

The challenges DDDAS seeks to advance include data modeling, context processing, and content application. To bring together data, context and content requires addressing issues in model fidelity such as how many parameters are needed for system control. When data is collected, it needs to be preprocessed to determine whether its inherent information matches the context. One example includes clutter reduction, sensor registration, and confuser analysis in vehicle tracking. Finally, another key challenge is that of sampling. Sampling is the multiresolution needed to monitor the situation, environment and network context to explain the content desired.


Handbook of Dynamic Data Driven Applications Systems

The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies.

Dynamic Data Driven Applications Systems: Third International Conference, DDDAS 2020, Boston, MA, USA, October 2-4, 2020, Proceedings

Call for Chapter Submissions for DDDAS-Book-II — Handbook on Dynamic Data Drive Application Systems (DDDAS) (Vol. II)

DDDAS In The News

Y. Qu, G. Chen, X. Liu, J. Yan, B. Chen and D. Jin, “Cyber-Resilience Enhancement of PMU Networks Using Software-Defined Networking,” 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ, USA, 2020, pp. 1-7, doi: 10.1109/SmartGridComm47815.2020.9303004.

  • Dynamic Data-Driven Decisions: Application to a Self-Aware UAV. We are developing the methods and algorithms that enable (1) on-board dynamic decision-making for an autonomous aerial vehicle, (2) creation of a vehicle Predictive Digital Twin, and (3) illustration of the benefits of Digital Thread. The distinguishing aspect of our approach is that it is both data-driven and physics-based. Our hypothesis is that by leveraging both physics-based knowledge (through physics-based simulation data and physics-based reduced models) and dynamic data (through on-board sensors) we can issue better decisions than if we were to use data alone. 

  • Developing a digital twin [ details ]
  • The PILOTS Programming Language: PILOTS is a Programming Language for spatiO-Temporal data Streaming applications, especially designed to be used for building applications that run on moving objects such as airplanes, cars, and so on. With very high-level specifications, users can easily build applications that take spatio-temporal data streams as an input and produce streams as outputs for use by other applications such as actuator controls, data mining/analyses/visualization, and error correction codes. These applications can be treated as components of a larger stream processing system.