Uncertainty Quantification in Computational Science and Engineering

Description from DukeHub

This course is concerned with the modeling, identification, and propagation of model and parametric uncertainties in computational science and engineering. The aim is to provide decision makers, engineers and scientists with predictions endowed with measures of confidence. In practice, the randomness introduced within the modeling framework can reflect intrinsic stochasticity or some lack of knowledge. The covered material finds applications in a broad range of fields, from the modeling of materials and complex systems to robust design optimization. The course is oriented towards the understanding and implementation of state-of-the-art techniques for applied or fundamental research projects.

Skills

• data and statisticsdescription: "modeling" and 2 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "decision"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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