You are here

Generalized Polynomial Chaos for Data-Driven Model Generation and Validation

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-22-C-7507
Agency Tracking Number: B21B-T003-0067
Amount: $154,970.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA21-T003
Solicitation Number: 21.B
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2021-12-06
Award End Date (Contract End Date): 2022-06-05
Small Business Information
1410 Sachem Place Suite 202
Charlottesville, VA 22901-2559
United States
DUNS: 120839477
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Neha Gandhi
 (434) 973-1215
 gandhi@bainet.com
Business Contact
 B. Eugene Parker
Phone: (434) 973-1215
Email: barron@barronassociates.com
Research Institution
 Virginia Polytechnic Institute and State University
 Trudy Riley
 
Office of Sponsored Programs, 300 Turner Street NW - Suite 4200
Blacksburg, VA 24061-6100
United States

 (540) 231-5727
 Nonprofit College or University
Abstract

Barron Associates, in partnership with the Hume Center at Virginia Tech, proposes to develop a comprehensive model generation and validation approach that leverages recent advances in generalized polynomial chaos (gPC) theory. Similar to neural-network-based artificial intelligence/machine learning (AI/ML) approaches, solutions based on gPC theory learn by adjusting the weights in a linear combination of nonlinear functions that operate on input data. gPC offers a number of distinct advantages over typical AI/ML formulations, which are important for this application. Foremost among them is that, gPC representations can be learned from analytical models, high-fidelity simulation data, hardware-in-the-loop data, flight-test data, or any combination thereof. Moreover, algorithms for learning parameters of a gPC model have a very direct and computationally efficient form. This efficient form can be used to conduct the repeated model generation and evaluation steps required for cross-validation. Once a model structure has been defined, each new batch of available data can be used to refine and validate the model. In Phase II, a general-purpose model generation and validation toolset will be constructed that packages these techniques in an easy-to-use software application. Approved for Public Release | 21-MDA-11013 (19 Nov 21)

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

US Flag An Official Website of the United States Government