Abstract :
Summary form only given. The presentation will discuss DDDAS, a paradigm which unifies systems´ modeling and instrumentation aspects, and is creating new and revolutionary capabilities for improved understanding, analysis, and optimized, autonomic management and decision support of operational of engineered and natural multi-entity systems, and including human and societal systems. Key underlying concept in DDDAS is the dynamic integration of instrumentation data and executing models of the system in a feedback control loop - that is on-line data are dynamically incorporated into the systems´ executing model, to improve the modeling accuracy or to speed-up the simulation, and in reverse the executing model controls the instrumentation to selectively and adaptively target the data collection process, and dynamically manage collective sets of sensors and controllers. DDDAS is timely with the advent of Large-ScaleDynamic-Data and Large-Scale-Big-Computing. Large-Scale-Dynamic-Data encompasses the next wave of Big Data, and namely dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems, through multitudes of heterogeneous sensors and controllers instrumenting these systems, and where the opportunities and challenges at these “large-scales” relate not only to the size of the data but the heterogeneity in data, data collection modalities, data fidelities, and timescales, ranging from real-time data to archival data. In tandem with this important dimension of dynamic data, there is an extended view of Big Computing, which includes a new dimension of computing - the collective computing by networked assemblies of multitudes of sensors and controllers. The DDDAS paradigm, driving and exploiting these notions of Large-Scale Dynamic Data and LargeScale Big Computing, is shaping research directions and engendering transformative impact in a range of natural and engineered systems application areas. Spanning environments from the nanoscale to the terra-scale and the extra-terra-scale environments, examples of advances and new capabilities that will be presented include: materials analysis and decision support for structural systems; manufacturing systems; cellular, neural, and biorobotic systems; environmental systems; critical infrastructure systems, such as urban and air transportation, energy powergrids, and smart agriculture.
Keywords :
"Adaptation models","Data models","Instruments","Data collection","Sensor systems"
