APPLICATION OF SPARSE DECOMPOSITION, DIMENSION REDUCTION, AND EMERGENT FEATURE RECOVERY TECHNIQUES TO LEARNING FROM MASSIVE PHYSICS BASED SIMULATION

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$100,000.00
Award Year:
2012
Program:
STTR
Phase:
Phase I
Contract:
FA9550-12-C-0058
Award Id:
n/a
Agency Tracking Number:
F11B-T27-0175
Solicitation Year:
2011
Solicitation Topic Code:
AF11-BT27
Solicitation Number:
2011.B
Small Business Information
Suite 101, 14900 Sweitzer Lane, Laurel, MD, 20707-
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
141629712
Principal Investigator:
Jeffrey Sieracki
President, CTO
(301) 604-0001
govcontact1@sr2group.com
Business Contact:
Craig Silver
Contracts Manager
(301) 467-8329
govcontact2@sr2group.com
Research Institution:
University of Maryland, College Par
Monique Anderson
3112 Lee Building
University of Maryland
College Park, MD, 20742-0742
(301) 405-6272
Nonprofit college or university
Abstract
ABSTRACT: We investigate in the context of massive-scale CFD simulation datasets, modern information extraction and feature discovery methods recently developed in the context of analyzing complex, high-dimensional sensor data. We include four task threads: (1) Dimensionality-reduction to examine correlation of emergent feature spaces with physical phenomena. (2) Identification and tracking of coherent features sets across time-steps. (3) Machine learning of correlations between emergent structures and measurable scalar parameters like lift and drag. (4) Use of simulation data to discover signature characteristics detectable by practical physical sensors. With significant experience in applying the subject algorithms to other classes of data, we can offer a cost-effective, high-return program by evaluating several interrelated approaches in single, a multi-thread effort. We exploit cutting edge mathematics related to compressive sensing and emerging concepts in data-adaptive, sparse image and signal processing. SR2 Group is teaming with Prof. Wojciech Czaja of The Norbert Wiener Center For Harmonic Analysis and Applications at the University of Maryland, and Dr. Elias Balaras of The George Washington University. We bring significant experience in DOD and civilian applications of these algorithms coupled with world-class expertise on large-scale CFD simulations. BENEFIT: This effort is synergistic with in-house algorithm development programs and will contribute to advancing capability across a spectrum of applications. Specific methods addressed in this proposal have two obvious markets: (1) Government applications, including DOD multi-physics simulations (2) and commercial CFD software users. CFD in itself spans numerous industries, including aerospace, automotive, naval architecture, and even sports and recreational equipment. Related market opportunities exist in providing a combinations of proprietary software and signature processing expertise to help solve difficult problems in mapping of emergent features in CFD or other complex multi-physics, chemical, or life-science models to macroscopic phenomena.

* information listed above is at the time of submission.

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