Reduced Order Non-INtrusive modeling methodology formulation and application for mission analysis

Abstract

The Department of Defense (DOD) has documented a strategic gap how exploratory analyses are accomplished to support capability development which was decomposed into areas of needed focused research. We began with an exploration of current methods for integrating different models to meet the concerns of Congress with regard to quality, accuracy, and dependability, noting that they have become too computationally prohibitive for exploring large trade spaces. In addition, current model abstraction methods have difficulty accounting for the increasing dimensionality associated with increasingly complex simulations. These observations led to the formulation of the Reduced Order Non-INtrusive (RONIN) modeling methodology, which generates predictive reduced order surrogate models, which capture more information regarding behaviors as compared to traditional methods. The RONIN modeling methodology works to create surrogate models which emulate stochastic full-order models (FOMs) by leveraging order reduction approaches, stochastic modeling methods, and regression techniques. To demonstrate the RONIN modeling methodology, a notional United States Air Force use case was defined, and a DOD standard simulation framework was used to create relevant simulation scenario which output a set of response distributions. 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 on average over 99% of the time while reducing the time needed to generate a distribution of outputs from minutes down to less than a second.