University at Buffalo 3 credits Mechanical & Aerospace Engr
This course offers an introduction to the fundamental principles and methodologies of uncertainty quantification (UQ) in predictive computational modeling for physical phenomena and engineering systems. The focus is on learning from data?whether from experiments, imaging, or high-resolution simulations?with a rigorous assessment of the reliability and accuracy of predictions from both physics-based and machine learning models. The course covers key methods for model validation and UQ, along with the computational algorithms necessary for implementation. Topics include frequentist and Bayesian probability theories, global sensitivity analysis, Bayesian approaches to model calibration, validation, and selection, as well as information theory, maximum entropy, Monte Carlo methods, and real-world applications in micro-scale materials, multi-species biological systems, and other complex systems.
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