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| Content Provider | Springer Nature Link |
|---|---|
| Author | Tempone, Raúl Tamellini, Lorenzo Haji Ali, Abdul Lateef bile, Fabio |
| Copyright Year | 2016 |
| Abstract | We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a suitable expansion. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the space of random data, and naturally, the error analysis uses the joint regularity of the solution with respect to both the variables in the physical domain and parametric variables. In MISC, the number of problem solutions performed at each discretization level is not determined by balancing the spatial and stochastic components of the error, but rather by suitably extending the knapsack-problem approach employed in the construction of the quasi-optimal sparse-grids and Multi-index Monte Carlo methods, i.e., we use a greedy optimization procedure to select the most effective mixed differences to include in the MISC estimator. We apply our theoretical estimates to a linear elliptic PDE in which the log-diffusion coefficient is modeled as a random field, with a covariance similar to a Matérn model, whose realizations have spatial regularity determined by a scalar parameter. We conduct a complexity analysis based on a summability argument showing algebraic rates of convergence with respect to the overall computational work. The rate of convergence depends on the smoothness parameter, the physical dimensionality and the efficiency of the linear solver. Numerical experiments show the effectiveness of MISC in this infinite dimensional setting compared with the Multi-index Monte Carlo method and compare the convergence rate against the rates predicted in our theoretical analysis. |
| Ending Page | 1605 |
| Page Count | 51 |
| Starting Page | 1555 |
| File Format | |
| ISSN | 16153375 |
| e-ISSN | 16153383 |
| Journal | Foundations of Computational Mathematics |
| Issue Number | 6 |
| Volume Number | 16 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2016-08-26 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Multi-level methods Economics Sparse grids Infinite dimensional integration Multivariate approximation Uncertainty quantification Linear and Multilinear Algebras, Matrix Theory Multi-level Finite element method Models, numerical methods Multi-index Stochastic Collocation Elliptic partial differential equations with random coefficients Random partial differential equations Approximation by polynomials Numerical Analysis Computer Science Stochastic Collocation methods Combination technique Applications of Mathematics Math Applications in Computer Science Finite elements, Rayleigh-Ritz and Galerkin methods, finite methods |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Analysis Computational Theory and Mathematics Computational Mathematics |
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