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M&S Uncertainty Quantification

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7407
Agency Tracking Number: B12B-007-0023
Amount: $99,998.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA12-T007
Solicitation Number: 2012.B
Timeline
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-02-20
Award End Date (Contract End Date): 2013-08-19
Small Business Information
4850 Hahns Peak Drive, Suite 200, Loveland, CO, -
DUNS: 956324362
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jason Adaska
 Principal Investigator
 (970) 612-2329
 jason.adaska@numerica.us
Business Contact
 John Bradbury
Title: Manager of Contracts&Counsel
Phone: (970) 612-2313
Email: john.bradbury@numerica.us
Research Institution
 Georgia Tech Applied Research Corp.
 Sophia Herbert-Peterson
 505 Tenth Street NW
North Buidling, Room 003
Atlanta, GA, 30318-
 (404) 385-6705
 Domestic nonprofit research organization
Abstract
A goal of Uncertainty Quantification (UQ) is to use computer simulation of complex systems to make scientifically informed assessments for high-consequence decisions. Because end-to-end empirical data is difficult to obtain for the Ballistic Missile Defense System (BMDS), computer simulation provides the best method for understanding BMDS capabilities against a wide range of threats. Numerica Corporation, in partnership with the Georgia Tech Research Institute, proposes to develop an adaptive, sampling-based framework for quantifying the impact of both aleatoric and epistemic uncertainties within the BMDS. The primary goal of this effort is to construct a simulator-agnostic UQ capability that is applicable to a broad range of modeling and simulation environments. Nonetheless, the Phase I effort would also implement a proof-of-concept prototype in a specific environment, to demonstrate the proposed technology on a problem of interest to MDA. Key innovations in the proposed work include: (i) novel sampling methods for efficiently exploring high-dimensional uncertainty spaces with dependent variables, (ii) non-parametric techniques for quantifying the errors due to finite sample size, (iii) feedback mechanisms to reduce the total number of samples needed for UQ, and (iv) flexible techniques for accelerating simulations through computation re-use.

* Information listed above is at the time of submission. *

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