Dynamic Data Driven Applications Systems (DDDAS) 6th International Conference

The DDDAS2026 conference will showcase the latest advancements in enhanced surrogate modeling and "systems analytics" as well as the development and deployment of "plausible AI"-that is, AI that is near-term consistent, human trustworthy, high-quality, and adaptable to changing conditions. By integrating data reasoning with rigorous scientific, systems cognizant models, the conference aims to establish DDDAS frameworks that advance Science and Technology methods, address challenges, and facilitate cross-cutting capabilities that deliver flexible and scalable approaches. The DDDAS/InfoSymbiotics paradigm advances both foundational and applied methods with system-aware models, including multimodal, multiscale, and coupled system approaches. It integrates uncertainty quantification, estimation, planning, control, and intelligent decision-makingâ€"often using machine learning and neural networks as tools. DDDAS-based methods support “systems-analytics†and “Dynamic/Predictive Digital Twin†capabilities, impacting fields like aerospace, biomedical, cyber, geosciences, space sciences, computer vision, medical science, and critical infrastructures security and performance, for a wide scope of applications. DDDAS2026 has a broadened scope in including methods that instantiate the DDDAS concept whether using sensor-based signal and data fusion (such as in the instrumentation feedback loop) or other instrumentation as well as other models generated data, AI machine learning and neural networks, or physics-based laws and principles. The DDDAS emphasis on dynamically coupled data and models, as well as their analysis, prediction of behavior and operational control, together with AI methods, entail advances in fundamental areas and permeate many topics of contemporary interest. By integrating dynamic data and models and models with AI, the conference aims to highlight advances in analysis, prediction, and operational control relevant to many current topics such as Engineering, Security, Healthcare, and Renewable Energy. Areas of interest include foundational and applications methods: Coupled DDDAS and AI â€" theory and methods, and applications; DDDAS and AI for analysis, prediction of behavior, and operational control of systems; Dynamic control of complex, heterogeneous, multimodal, distributed systems; Systems Analytics and beyond Big-Data analytics; Dynamic Digital Twins (also referred to as DDDAS-based DTs, and Predictive DTs); Systems-cognizant representations/modeling (e.g., physics-based modeling, agent, graph-based) coupled with ML/NN methods; Informative approaches for Estimation, Control and (Machine) Learning, Planning and Decision support Multimodal learning; Optimization methods (beyond/additionally-to deep learning); Multimodality decision making/decision support; Information fusion and inference; Advanced Computer Vision methods; Planning and control; Efficient and Scalable methods for stochastic systems, modeling, simulation, and sensing; Learning, optimization and awareness methods; Explainable and Interpretable Methods; Development of architectures of DDDAS-based reliable AI; Coupled dynamic sensors with dynamic decision making and prediction for critical applications; Adversarial attack and defense, and modeling threat-awareness effects; Novel Scientific Applications enabled through methods above, including areas such as: Materials â€" Fundaments & Design â€" Structural Health Monitoring â€