AMCS 350

AMCS 350 - Spectral Methods for Uncertainty Quantification  By Prof. Omar Mohamad Knio

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This course is an advanced introduction to uncertainty propagation and quantification in model-based simulations. Examples are drawn from a variety of engineering and science applications, emphasizing systems governed by ordinary or partial differential equations.  The course will emphasize a probabilistic framework, and will survey classical and modern approaches, including sampling methods and techniques based on functional approximations.

Students completing this course will be able to: (1) apply classical and modern methods for prop- agating parametric uncertainties in computational simulations in problems governed by ordinary and differential equations, (2) quantify uncertainties in predicted outputs; (3) identify dominant sources, (4) identify courses for uncertainty reduction, and (5) apply UQ concepts to problems of inference or inverse design.

Enrolled students can access course material through KAUST's Blackboard via: