The purpose of model-based experimental design is to maximise the information gathered for quantitative model identification. Instead of the commonly used optimal experimental design, robust experimental design aims to address parametric uncertainties in the design process. In this paper, the Bayesian robust experimental design is investigated, where both a Monte Carlo sampling strategy and loc...

Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher-order moments for quantities of interest, e.g., generating cost, can require a prohibitively large number of samples. We propo...

In this paper, we develop a control algorithm to generate entanglement in a quantum system with uncertainties. The system under consideration is an uncertain system of two twolevel atoms interacting with each other through a dipole-dipole interaction. The sampling-based learning control (SLC) strategy is employed to find a control law. An SLC strategy contains two steps of training and evaluati...

Many countries receive shipments of bulk cereals from primary producers. There is a volume of work that is on-going that seeks to arrive at appropriate standards for the quality of the shipments and the means to assess the shipments as they are out-loaded. Of concern are mycotoxin and heavy metal levels, pesticide and herbicide residue levels, and contamination by genetically modified organisms...

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification and data mining analysis. RAVEN is currently equipped with three different sampling strategies: Once-through samplers (Monte Carlo, Latin Hyper Cub...