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DEVS-NN: Efficient Development of Data Driven Models through Hybrid DEVS/SES/Neural Network Methodology

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-22-C-7505
Agency Tracking Number: B21B-T003-0025
Amount: $154,997.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA21-T003
Solicitation Number: 21.B
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
6909 W Ray Rd STE 15-107
Chandler, AZ 85226-1699
United States
DUNS: 190919030
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Bernard Zeigler
 (520) 220-8811
Business Contact
 Doohwan Kim
Phone: (602) 334-6649
Research Institution
 Auburn University
 James Weyhenmeyer
Research Inn Ctr, 540 Devall Drive, Suite 200
Auburn, AL 36832-5888
United States

 (334) 844-4438
 Nonprofit College or University

DEVS-NNtakes two complementary approaches to reduce the amount of data that must be collected for artificial intelligence/machine learning (AI/ML): 1. Use existing knowledge (in the form of a Discrete Event System Specification (DEVS) simulation model created by subject matter experts) to reduce the “work” a neural network (NN) has to do. The DEVS simulation model gets close to the correct output. The neural network needs less training data because instead of creating output from scratch it can use the DEVS simulation model output as a starting point. 2. A DEVS ontology (provided by System Entity Structures (SES)) is used to guide sampling/data collection. As we will detail later, a further advantage of our approach is that the SES ontology can provide additional validation benefits over unguided sampling/data collection approaches. Intelligently guiding the sampling/data collection process means less data needs to be collected. Our approach is most applicable to data collection from hardware-in-the-loop and high-fidelity physics simulations. In addition to being expensive, both of these data sources allow for a large degree of control over the scenarios that are simulated. The SES ontology will organize the set of possible scenarios; also called the scenario space. Both data sources have large scenario spaces, which will benefit from being organized using an SES ontology. Both data sources can be efficiently simulated using DEVS simulation models. The scenario space can be defined using SES, then programmatically pruned into a Pruned Entity Structure (PES), which can subsequently be automatically converted into a runnable DEVS simulation model. All of this can be implemented within RTSync’s proprietary MS4 Me DEVS Integrated Development Environment (IDE). Approved for Public Release | 21-MDA-11013 (19 Nov 21)

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

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