Course Information

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Course Topics

• Model development including deterministic and stochastic models
• Numerical approximation techniques and convergence analysis
• Parameter estimation techniques
• Data collection techniques and experimental analysis
• Uncertainty in parameters
• Monte Carlo techniques to predict uncertainty
• Statistical and residual analysis
• Gaussian and Bayesian emulators

Grade: Projects (can be done in groups of up to 3 or 4 students): 100%

Office hours: By appointment