University of Texas at Austin
P.O Box 7726
Austin, TX 78713 512/471-6424
NSF Program(s):
ITR-DYNAMIC DATA DRIV APP SYS, DYNAMIC DATA DRIVEN APPL SYSTS, COMPUTER SYSTEMS, COLLABORATIVE RESEARCH
Field Application(s):
0000912 Computer Science
Program Reference Code(s):
HPCC, 9218, 7481, 7298, 5980, 5955, 2884
Program Element Code(s):
T363, 7581, 7481, 7354, 7298
ABSTRACT
The project will develop a multiscale, data-driven, high performance computational framework for real-time reconstruction of hazardous events from sparse measurements, and consequent probabilistic prediction of the evolution of the hazard. The framework is distinguished by four phases that are performed continually with dynamically-obtained data over the lifetime of the hazardous event. (1) Measurement: Distributed sensors provide dynamic measurements over a specified time horizon that will be used to reconstruct the initial conditions of the event. (2) Inversion: Driven by the sparse measurements, an inverse problem is solved to estimate the initial conditions for the equations governing the evolution of the hazard. (3) Prediction: Statistical analysis of the inversion results permits estimation of the uncertainty in the initial conditions, which is propagated into a prediction of the evolution of the hazard and its uncertainty. (4) Steering: Sensors are steered to new locations based on an effectivity index that incorporates sensitivities of the inversion with respect to sensor location, estimated uncertainty in the prediction, and population density factors. Continual application of the measure-invert-predict-steer (MIPS) framework described above results in updated predictions of the evolving hazard with built-in uncertainty estimates, as well as revised sensor deployment strategies that refine the predictions to reduce their uncertainty. The methods developed consider two time scales of decision making at which the MIPS framework must execute. The seconds-to-minutes decision-making scale is required by first responders to begin immediate response efforts. For such time scales, high-fidelity models in the form of partial differential equations (PDEs) are too formidable. Instead, the proposed methods will construct reduced-order models of the PDEs to facilitate realtime execution of the MIPS framework. The minutes-to-hours decision-making scale permits more careful and measured response by emergency officials using high-fidelity, high-resolution PDE models. To enable rapid execution of the MIPS cycle for such models, the project will develop fast, scalable, parallel algorithms for inversion and prediction. To demonstrate, assess, harden, robustify, and the resulting framework, will be validated on a specific application testbed: prediction of the urban/regional dispersion of intentionally- or accidentally-released atmospheric contaminants from sparse measurements.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Akcelik, V; Biros, G; Draganescu, A; Ghattas, O; Hill, J; Waanders, BV. "Inversion of airborne contaminants in a regional model," COMPUTATIONAL SCIENCE - ICCS 2006, PT 3, PROCEEDINGS, v.3993, 2006, p. 481-488.
Bashir, O; Willcox, K; Ghattas, O; van Bloemen Waanders, B; Hill, J. "Hessian-based model reduction for large-scale systems with initial condition inputs," International Journal for Numerical Methods in Engineering, v.73, 2008, p. 844.
O. Bashir, O. Ghattas, J. Hill, B. van Bloeman Waanders, and K. Willcox. "Hessian-based model reduction for large-scale data asssimilation problems," International Conference on Computational Science, 2007.
T. Bui-Thanh, K. Willcox, and O. Ghattas. "Parametric reduced-order models for probabilistic analysis of unsteady aerodynamics applications," AIAA Journal, v.46, 2008, p. 2520.
T. Bui-Thanh, K. Willcox, and O. Ghattas. "Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space," SIAM Journal on Scientific Computing, v.30, 2008, p. 3270.
T. Bui-Thanh, K. Willcox, O. Ghattas, B. van Bloemen Waanders. "Goal-oriented, model-constrained optimization for reduction of," Journal of Computational Physics, v.224, 2006, p. 880.
CONFERENCE PROCEEDINGS PRODUCED AS A RESULT OF THIS RESEARCH
Bashir, O; Ghattas, O; Hill, J; Waanders, BV; Willcox, K. "Hessian-based model reduction for large-scale data assimilation problems," in 7th International Conference on Computational Science (ICCS 2007)., v.4487, 2007, p. 1010-1017.
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