Development of Generalized Mapping Tools to Improve Implementation of Data Driven Computer Simulations. (04-ERD-083)

摘要

The Stochastic Engine (SE) is a data driven computer simulation tool for predicting the characteristics of complex systems. The SE integrates accurate simulators with the Monte Carlo Markov Chain (MCMC) approach (a stochastic inverse technique) to identify alternative models that are consistent with available data and ranks these alternatives according to their probabilities. Implementation of the SE is currently cumbersome owing to the need to customize the pre-processing and processing steps that are required for a specific application. This project widens the applicability of the Stochastic Engine by generalizing some aspects of the method (i.e. model-to-data transformation types, configuration, model representation). We have generalized several of the transformations that are necessary to match the observations to proposed models. These transformations are sufficiently general not to pertain to any single application. This approach provides a framework that increases the efficiency of the SE implementation.