Design And Analysis Of Simulation Experiments

Catalog description

A review of currently accepted practices in design of simulation experiments, with validation and outcome analysis, and new techniques for model evaluation. Techniques covered may include methods for uncertainty quantification in deterministic models, design of experiments to match field experiments, data collection and sampling methods, data reduction methods, and imaging and statistical visualization. Course may be offered in classroom based, distance, or hybrid formats. Typically offered Fall Spring Summer. Course Learning Outcomes 1. Identify problems to simulate. 2. Choose a simulation based model appropriate for a problem on the basis of current research. 3. Analyze the data from simulation results. 4. Compose and present a simulation or modeling project efficiently designed with clear visual presentation of outcomes and validation of results. View Class Schedule