In the book Handbook of dynamic data driven applications systems. Vol. 2
Springer, Cham, Switzerland
Summary
This chapter presents a dynamic data-driven computational framework for end-to-end measurement, inversion, prediction, and sensor steering (MIPS). The advances enabled through the MIPS approach presented here are demonstrated in the context of two applications and their respective testbeds. The first application targets the prediction of the urban/regional dispersion of atmospheric contaminants from sparse measurements. The second application addresses structural health monitoring onboard an unmanned aerial vehicle that undergoes a damage event. The methodological elements of the DDDAS-based methods underpinning the MIPS approach include a Bayesian formulation of the inverse problem that yields a statistical characterization of the estimated parameters, probabilistic propagation of the estimated parameters to yield a prediction of the evolution of the hazard and its uncertainty, formulation of the task of sensor steering as an optimization problem that seeks to minimize prediction uncertainty, and a model reduction approach that facilitates execution of all of these steps in a real-time setting.
Keywords: Atmosphere; Damage; Sensor steering; Structural health monitoring; Unmanned aerial vehicle; Weather
ISBN: 978-3-031-27985-0
DOI: 
https://doi.org/10.1007/978-3-031-27986-7_8
DOI of the book:
https://doi.org/10.1007/978-3-031-27986-7
Published: 2023
Citation:
S. Wogrin, A. Singh, D. Allaire, O. Ghattas, K. Willcox, From data to decisions: a real-time measurement–inversion–prediction–steering framework for hazardous events and health monitoring, in Handbook of dynamic data driven applications systems. Vol. 2. Ed. Springer. Cham, Switzerland, 2023.
IIT-23-474L
