NARC -- Autumn Quarter 2011
Uncertainty QuantificationMain NARC page
We will meet Thursdays 1:30 - 2:20pm in GUG 415L.
Tentative schedule and slides from talks:
- 29 September 2011: Introduction by Randy LeVeque [uq1.pdf]
- 6 October 2011: Guang Lin, Uncertainty Quantification and Its Application in Energy and Environmental related complex systems [uq2.pdf]
- 13 October 2011: Andrea Hawkins-Daarud and Alex Konomi, Introduction to Bayesian statistics
- 20 October 2011: Monte-Carlo methods and Hastings-Metropolis algorithm
- The Bayesian Choice, Chapter 1 link
Introduction to Bayesian Scientific Computing Ten Lectures on
Subjective Computing, (Free online)
Calvetti, Daniela, Somersalo, E.
- Jeff Hart's Bayesian Statistics course at Texas A&M Notes See in particular sections 1, 4, 5, 6, 10, 17 of these notes.
International Journal for Uncertainty Quantification
from a workshop at the Stanford Center for Integrated Turbulence
- An Introduction to Stochastic Differential Equations Version 1.2,
by L.C. Evans, UC Berkeley class notes
Lecture notes from L.C. Evan's webpage.
Review Article: Fast Numerical Methods for Stochastic Computations,
by Dongbin Xiu, Communications in computational physics, vol 5, No 2-4, pp242-272.
- Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids,
by G Lin and A Tartakovsky, J. Comput. Phys. 229 (2010) 6995-7012.
- Quantification of uncertainty in computational fluid dynamics,
by P. J. Roache,
Annual Review of Fluid Mechanics
29 (1997) pp. 123-160.
- High-order collocation methods for differential equations with random inputs
by D. Xiu and J. Hesthaven, SIAM J. Sci. Comput. 27 (2005), pp. 1118-1139.
- Modeling uncertainty in flow simulations via generalized polynomial chaos,
by D. Xiu and G. E. Karniadakis, J. Comput. Phys 187 (2003), pp. 137-167.
Numerical Methods for Stochastic Computations: A Spectral Method Approach,
Monte Carlo Strategies in Scientific Computing
Liu, Jun S.
Geof H. Givens and Jennifer A. Hoeting
Introducing Monte Carlo Methods with R (Free online)
Christian Robert and George Casella
R.G. Ghanem and P.D. Spanos. Stochastic Finite Elements: A Spectral Approach.
Marzouk, Y.M., Xiu, D.B. A stochastic collocation approach to
Bayesian inference in inverse problems. Communications in
Computational Physics 6: 826-847 (2009).
- Sandia Labs
- Dakota software