Rapid, Low Cost Modeling and Simulation: Integrating Bayesian and Neural Networks as an Alternative to an Equation-Based Approach

Modeling and Simulation is an important tool in many fields of science and engineering. When properly used, it can save time and money as compared to prototypes or experiments. Current practice is to use an equation-based approach. However, equation-based models that accurately represent the real world can require extensive time and money to construct. This research was conducted to explore alternate methods of creating accurate models and simulations that could be done rapidly and at much lower cost. The research compared engineering modeling applications for time of construction and the accuracy between equation-based models and three methods of Bayesian network construction: human judgment, formulae and computer-generated with neural network integration. While the human judgment and formulae models showed no advantages, the computer-generated Bayesian networks were both much faster to construct and more accurate as compared to equivalent equation-based models.