STAT-6544: Surrogate Modeling

Description: Statistical techniques at the interface between mathematical modeling via computer simulation, computer model meta-modeling (i.e., emulation/surrogate modeling), calibration to field data, and geometric and model-based sequential design, and Bayesian optimization. Historical literature, canoncial examples, and modern nonparametric methods like Gaussian processes. Computation and implementation, fidelity enhancements and approximate methods for big data. Real-world field experiments and computer model simulations from the physical and engineering sciences.

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Course Hours: 3 credits

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Crosslist: N/A

Repeatability: N/A