Real Time Estimation and Prediction Using Optimistic Simulation and Control Theory Techniques

摘要

This concept paper provides a theoretical foundation for developing high performance parallel and distributed simulations that can be used to (1) estimate the state of a modeled system based on real-time inputs, (2) predict future multi-hypothesis outcomes based on those estimates, and (3) perform estimation and prediction capabilities in a parallel and distributed computing environment while achieving computational speedup. Kalman Filtering techniques are first introduced to provide the necessary technical background on estimation and prediction control theory used in the rest of the paper. Following the discussion of Kalman Filters, this paper then shows how these control theory concepts can be applied to rollback-based parallel and distributed simulations to facilitate real-time estimation and prediction. Three very important research topics are then discussed: (1) handling multiple hypotheses branching within the simulation, (2) using statistical algebra to represent state variables as statistical distributions rather than relying on random number generation and multiple independent replications to support Monte Carlo models, and (3) metrics for defining raw and relative measures of effectiveness and performance. Finally, challenging related issues and research topics are discussed, such as where bottlenecks might arise and how using Field Programmable Gate Arrays (FPGAs) can alleviate those performance problems through the use of specialized hardware acceleration.