Course Information

Help


Local links

process


MA 797

Uncertainty Quantification for Physical and Biological Models





Background and Motivation

  • Lecture 1: Motivation and Prototypical Examples (PDF)

Probability and Statistics Background

  • Lectures 2 and 3: Random variables, estimators and sampling distributions (PDF)

Representation of Random Inputs

  • Lecture 4: Representation of random variables and random fields
  • Lecture 5: Karhunen-Loeve Expansions (PDF)

Parameter Selection Techniques

  • Lecture 6: Parameter selection techniques (PDF)
  • Lecture 7 and 8: Global Sensitivity Analysis (PDF)

Statistical Model Calibration

  • Lectures 9 and 10: Frequentist techniques for model calibration (PDF)
  • Lectures 11-13: Bayesian techniques for model calibration (PDF)

Uncertainty Propagation in Models

  • Lectures 14 and 15: Random sampling, perturbation methods and prediction intervals (PDF)
  • Lectures 16 and 17: Stochastic spectral methods (PDF)
  • Lectures 18 and 19: Sparse grid quadrature and interpolation (PDF)
  • Lecture 20: Sparse grid example (PDF)

Surrogate Models

  • Lectures 21 and 22: Surrogate and Reduced-Order Models (PDF)