In this project we consider the development of a Cyber Physical Freight Transportation System for load balancing in multimodal transportation networks. We use on line simulation models to capture the nonlinear and complex dynamical characteristics of the transportation networks. The simulation models generate the states of the network that are used to solve an optimization problem which finds the optimum routes.

A significant challenge in dimensional accuracy control of cyber-physical additive manufacturing systems (CPAMS) is the specification of geometric shape deviation models. The current practice of constructing tailor-made deviation models for each combination of computer- aided design model, additive manufacturing (AM) process, and process setting is impractical and inefficient for general application in CPAMS. We present a new framework and class of  Bayesian neural networks for automated and efficient deviation model building in CPAMS.