Knowledge Bank

Leveraging the computational power of modern computer systems to give the war fighter the edge is pivotal to the completion of Objective 1 of the USAF's 2030 R&D strategy. The Advanced Framework for Simulation, Innovation, and Modeling (AFSIM) simulates, via Monte-Carlo methods, the varying levels of complexity, ranging from the component level to full campaigns, that are necessary for a complete system. Due to the nature of these types of simulations, it is necessary that the information retrieved be both of statistical significance and of thoughtful value. To determine statistical significance, convergence over iterations is monitored and terminated upon reaching user-defined requirements. To ensure thoughtful value to the user, determining the level of risk of an operation is crucial. Within the current scope, one such way to determine risk is by determining the risk of a units position in relation to it's absolute (map) position, and relative position. Accurately assessing this risk requires initial condition perturbations to have valuable data across the range of potential positional starting conditions. Once data is collected at varying levels of convergence and at varying perturbed conditions, combined together, provides the war fighting analyst data that can be utilized to generate strategies with information beyond experience. This information can be combined later with more information, such as traceability and Markov Chains, to add another level of risk understanding. Current processes have been brought to the level of monitoring convergence to the required levels. Perturbing initial conditions and combing data sets into valuable visualizations is still required.