Alexander Braafladt 

(Advisor: Prof. Dimitri Mavris] 

will defend a doctoral thesis entitled, 

Accelerated Simulation-Based Analysis of Emergent and Stochastic Behavior in Military Capability Design 

On 

Monday, June 5th at 2:00 p.m.  
Collaborative Visualization Environment (CoVE) 

Weber Space Science and Technology Building (SST II) 

And 

Click here to join the meeting 

Abstract 
In military capability design, the United States Air Force (USAF) is working to modernize to be ready to succeed in future operations. During the process, high-fidelity military simulation is used iteratively to build up understanding of complex military scenarios and consider technology and concept alternatives. While high-fidelity simulation is critical to the analysis, it is often expensive and time consuming to work with. In addition, the required pace for analysis needs to be accelerated as technology and threats rapidly evolve. In response to these challenges, the research in this thesis focuses on accelerating two central parts of simulation-based analysis in capability design. The first part focuses on improving methods for searching for emergent behavior, which is critical for building up understanding with simulation. The second part focuses on including stochastic responses from simulation in parametric models used during tabletop design exercises, which are critical for comparing alternatives. 

To accelerate simulation-based analysis of emergent behavior, a specific definition of emergent behavior is synthesized from the literature that prompts optimization approaches to be used for searching more quickly than with brute-force Monte Carlo Simulation (MCS). Specifically, searching for emergent behavior as rare, localized and stochastic extreme events is accelerated using novel Bayesian Optimization (BO) techniques that adaptively query the simulation to find rare events. In experiments with test problems based on the behavior expected with an Agent-Based Modeling (ABM) simulation approach, the new BO techniques show significant improvement over MCS. 

For accelerating analysis of stochastic behavior during tabletop design exercises, a surrogate modeling approach that uses Reduced-Order Modeling (ROM) techniques combined with a new field representation is developed. The surrogate modeling approach is shown to work effectively with distributions like those expected with military simulation, allowing parametric, interactive queries of distributions. 

A final demonstration of the techniques was completed using two scenarios developed in simulation with the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). First, a Suppression of Enemy Air Defenses (SEAD) scenario was used to demonstrate the effectiveness of the new techniques at searching for rare, localized extreme events. Second, a four vs. four air combat scenario was used to demonstrate the effectiveness of the new technique for searching for rare, stochastic extreme events, and to demonstrate the new distribution surrogate modeling approach. The results together support an accelerated methodology for iterative simulation-based analysis of military scenarios. 

Committee 

• Prof. Dimitri N. Mavris – School of Aerospace Engineering (advisor) 

• Dr. Alicia M. Sudol – School of Aerospace Engineering 
• Prof. Daniel P. Schrage– School of Aerospace Engineering 
• Dr. Mark S. Whorton – School of Aerospace Engineering, GTRI 
• Dr. Nicholas Hanlon – MS&A Branch, Air Force Research Laboratory, RQSA