Award Abstract #0540186
Collaborative Research: DDDAS-TMRP: MIPS: A Real-Time Measurement-Inversion-Prediction-Steering Framework for Hazardous Events

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 16, 2005
Latest Amendment Date:	April 3, 2006
Award Number:	0540186
Award Instrument:	Standard Grant
Program Manager:	Krishna Kant CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	October 1, 2005
Expires:	September 30, 2009 (Estimated)
Awarded Amount to Date:	$274998
Investigator(s):	Karen Willcox kwillcox@MIT.EDU (Principal Investigator)
Sponsor:	Massachusetts Institute of Technology 77 MASSACHUSETTS AVE Cambridge, MA 02139 617/253-1000
NSF Program(s):	ITR-DYNAMIC DATA DRIV APP SYS, COLLABORATIVE RESEARCH
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9218, 7481, 7298, 5955
Program Element Code(s):	T363, 7581, 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.

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