Abstract
ChapterĀ 12 considers a spectral approach to UQ, namely Galerkin expansion, that is mathematically very attractive in that it is a natural extension of the Galerkin methods that are commonly used for deterministic PDEs and (up to a constant) minimizes the stochastic residual, but has the severe disadvantage that the stochastic modes of the solution are coupled together by a large system such as (12.15).
[W]hen people thought the Earth was flat, they were wrong. When people thought the Earth was spherical, they were wrong. But if you think that thinking the Earth is spherical is just as wrong as thinking the Earth is flat, then your view is wronger than both of them put together.
The Relativity of Wrong
Isaac Asimov
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
As usual, readers will lose little by assuming that \(\mathcal{U} = \mathbb{R}\) on a first reading. Later, \(\mathcal{U}\) should be thought of as a non-trivial space of time- and space-dependent fields, so that \(U(t,x;\theta ) =\sum _{k\in \mathbb{N}_{0}}(t,x)\varPsi _{k}(\theta )\).
- 2.
Indeed, many standard numerical linear algebra packages will readily solve the system (13.4) without throwing any error whatsoever.
References
V.Ā Barthelmann, E.Ā Novak, and K.Ā Ritter. High dimensional polynomial interpolation on sparse grids. Adv. Comput. Math., 12(4):273ā288, 2000. doi: 10.1023/A:1018977404843.
M.Ā D. Buhmann. Radial Basis Functions: Theory and Implementations, volumeĀ 12 of Cambridge Monographs on Applied and Computational Mathematics. Cambridge University Press, Cambridge, 2003. doi: 10.1017/ CBO9780511543241.
S.Ā Conti, J.Ā P. Gosling, J.Ā E. Oakley, and A.Ā OāHagan. Gaussian process emulation of dynamic computer codes. Biometrika, 96(3):663ā676, 2009. doi: 10.1093/biomet/asp028.
C.Ā deĀ Boor. A Practical Guide to Splines, volumeĀ 27 of Applied Mathematical Sciences. Springer-Verlag, New York, revised edition, 2001.
C.Ā deĀ Boor and A.Ā Ron. On multivariate polynomial interpolation. Constr. Approx., 6(3):287ā302, 1990. doi: 10.1007/BF01890412.
D.Ā G. Krige. A statistical approach to some mine valuations and allied problems at the Witwatersrand. Masterās thesis, University of the Witwatersrand, South Africa, 1951.
O.Ā P. LeĀ MaĆ®tre and O.Ā M. Knio. Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics. Scientific Computation. Springer, New York, 2010. doi: 10.1007/ 978-90-481-3520-2.
G.Ā Matheron. Principles of geostatistics. Econ. Geo., 58(8):1246ā1266, 1963. doi: 10.2113/gsecongeo.58.8.1246.
A.Ā Narayan and D.Ā Xiu. Stochastic collocation methods on unstructured grids in high dimensions via interpolation. SIAM J. Sci. Comput., 34(3): A1729āA1752, 2012. doi: 10.1137/110854059.
F.Ā Nobile, R.Ā Tempone, and C.Ā G. Webster. A sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J. Numer. Anal., 46(5):2309ā2345, 2008a. doi: 10.1137/ 060663660.
F.Ā Nobile, R.Ā Tempone, and C.Ā G. Webster. An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J. Numer. Anal., 46(5):2411ā2442, 2008b. doi: 10.1137/070680540.
C.Ā E. Rasmussen and C.Ā K.Ā I. Williams. Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge, MA, 2006.
M.Ā Schober, D.Ā Duvenaud, and P.Ā Hennig. Probabilistic ODE solvers with RungeāKutta means. In Z.Ā Ghahramani, M.Ā Welling, C.Ā Cortes, N.Ā D. Lawrence, and K.Ā Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 739ā747. Curran Associates, Inc., 2014.
P.Ā L. Smith. Splines as a useful and convenient statistical tool. The American Statistician, 33(2):57ā62, 1979. doi: 10.1080/00031305.1979.10482661.
D.Ā Xiu. Numerical Methods for Stochastic Computations: A Spectral Method Approach. Princeton University Press, Princeton, NJ, 2010.
L.Ā Yan, L.Ā Guo, and D.Ā Xiu. Stochastic collocation algorithms using ā 1-minimization. Int. J. Uncertain. Quantif., 2(3):279ā293, 2012. doi: 10.1615/Int.J. UncertaintyQuantification.2012003925.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
Ā© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Sullivan, T.J. (2015). Non-Intrusive Methods. In: Introduction to Uncertainty Quantification. Texts in Applied Mathematics, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-319-23395-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-23395-6_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23394-9
Online ISBN: 978-3-319-23395-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)