Mark Bateman, Maj USSF
(Advisor: Prof. Dimitri Mavris)

will defend a doctoral thesis entitled,

Reduced Order Non-INtrusive (RONIN) Modeling for Strategic Defense Planning

On

Thursday, June 1st  at 12:00 p.m.
Collaborative Visualization Environment (CoVE)

Weber Space Science and Technology Building (SST II)

And
Click here to join the meeting

Abstract
The Department of Defense (DoD) along with research organizations like RAND has documented a strategic gap acknowledging the need for improvements in the ability to conduct exploratory analyses to support capability development that seek to exploit both technological and doctrinal conceptual solutions. In this work, the overarching strategic gap was decomposed into more focused areas of needed research, starting with an exploration of current integration methods of different models to meet the concerns of Congress with regards to quality, accuracy, and dependability; noting that they have become too computationally prohibitive to be used to explore a large design/decision space. Further observations noted that for complex and potentially nonlinear modeled performance qualities, passing expected values does not provide the needed traceability between different levels of model abstraction. Additionally, current model abstraction methods have difficulty accounting for the increasing dimensionality associated with increasingly complex models or simulations. These observations lead to the objective of this research, which is the formulation and demonstration of a methodology which leverages reduced order modeling (ROM) methods for traceable model abstraction that effectively and efficiently captures complex system-of-systems behaviors within current military operations modeling and simulation methods.

A review of current literature led to the derivation of the following requirements for ROMs: there is a need to account for nonlinear interdependencies, underlying physical phenomena, and stochastic effects. A set of research questions, hypotheses, and experiments was posed and completed to further understand and address identified gaps. All of which guided the formulation of the Reduced Order Non-INtrusive (RONIN) modeling methodology, which enabled the accomplishment of the stated research objective. The RONIN modeling methodology works to create and use predictive reduced order surrogate models which capture more information regarding behaviors and interactions as compared to traditional methods such as “look-up'' tables or simple passing of expected values. Finally, to demonstrate the RONIN modeling methodology's ability the meet the research objective, a notional United States Air Force use case was defined and a DoD standard simulation framework was used to generate a Full Order Model (FOM) which output a set of response distributions. This use case modeled a Suppression of Enemy Air Defense (SEAD) mission which could explore how different decisions and force structures effected the total number of friendly loses and enemy kills. Ultimately, the RONIN modeling method was used to create a predictive surrogate model which was able to reconstruct output distributions which are statically consistent with the original FOM output data.

Committee

• Prof. Dimitri N. Mavris – School of Aerospace Engineering (advisor)
• Prof. Daniel Schrage – School of Aerospace Engineering
• Prof. Graeme Kennedy – School of Aerospace Engineering
• Prof. John Colombi – Air Force Institute of Technology
• Dr. Alicia M. Sudol – School of Aerospace Engineering