WARPIV TECHNOLOGIES, INC. — Department of Defense STTR Phase I: MDA22-T001

Description

This topic identifies the need creating a Modeling and Simulation (M&S) development and execution environment that significantly decreases the time to execute statistically significant batches of stochastic simulation runs for the purpose of estimating scenario output and outcome distributions while improving statistical knowledge of the outcome distributions. The strategy sought by this topic is to explore the notion of paratemporal simulation techniques that explore probabilistic timeline branching, which can occur at critical decision or outcome points. WarpIV Technologies, Inc. has unique and critical technology within its scalable, composable, and intelligent WarpIV Kernel simulation engine necessary to support paratemporal simulation. These technologies are listed below and are described in our proposal in more detail. Five-dimensional simulation that efficiently explores all stochastic timelines in a single run Statistical Algebra Package that performs mathematical operations on variables representing distributions Optimization Engine that intelligently guides simulation excursions in concurrently executing batches This effort will establish a new foundation for supporting paratemporal simulation based on the revolutionary five-dimensional simulation technology provided in the WarpIV Kernel. Phase I will (a) leverage prior work that established a collection of composable missile and air defense engagement models, (b) extend engagements between interceptors and warheads with five-dimensional branching constructs, (c) extend the statistical algebra package to more correctly support correlations between variables in complex mathematical expressions, (d) extend the statistical algebra package to support matrix and vector operations, and (e) apply statistical algebra to a simple case involving a radar forming statistical algebra detections that feed into a range-tracking Kalman filter and verifying that the statistical properties match the standard approach involving random numbers. Approved for Public Release | 22-MDA-11339 (13 Dec 22)