838 research outputs found
IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains
This paper proposes an extension of the Multi-Index Stochastic Collocation
(MISC) method for forward uncertainty quantification (UQ) problems in
computational domains of shape other than a square or cube, by exploiting
isogeometric analysis (IGA) techniques. Introducing IGA solvers to the MISC
algorithm is very natural since they are tensor-based PDE solvers, which are
precisely what is required by the MISC machinery. Moreover, the
combination-technique formulation of MISC allows the straight-forward reuse of
existing implementations of IGA solvers. We present numerical results to
showcase the effectiveness of the proposed approach.Comment: version 3, version after revisio
Sparse-grid polynomial interpolation approximation and integration for parametric and stochastic elliptic PDEs with lognormal inputs
By combining a certain approximation property in the spatial domain, and
weighted -summability of the Hermite polynomial expansion coefficients
in the parametric domain obtained in [M. Bachmayr, A. Cohen, R. DeVore and G.
Migliorati, ESAIM Math. Model. Numer. Anal. (2017), 341-363] and [M.
Bachmayr, A. Cohen, D. D\~ung and C. Schwab, SIAM J. Numer. Anal. (2017), 2151-2186], we investigate linear non-adaptive methods of fully
discrete polynomial interpolation approximation as well as fully discrete
weighted quadrature methods of integration for parametric and stochastic
elliptic PDEs with lognormal inputs. We explicitly construct such methods and
prove corresponding convergence rates in of the approximations by them,
where is a number characterizing computation complexity. The linear
non-adaptive methods of fully discrete polynomial interpolation approximation
are sparse-grid collocation methods. Moreover, they generate in a natural way
discrete weighted quadrature formulas for integration of the solution to
parametric and stochastic elliptic PDEs and its linear functionals, and the
error of the corresponding integration can be estimated via the error in the
Bochner space norm of the generating methods
where is the Gaussian probability measure on and
is the energy space. We also briefly consider similar problems for
parametric and stochastic elliptic PDEs with affine inputs, and by-product
problems of non-fully discrete polynomial interpolation approximation and
integration. In particular, the convergence rate of non-fully discrete obtained
in this paper improves the known one
Multi-index Stochastic Collocation convergence rates for random PDEs with parametric regularity
We analyze the recent Multi-index Stochastic Collocation (MISC) method for
computing statistics of the solution of a partial differential equation (PDEs)
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. 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 PDEs in which the log-diffusion coefficient is modeled as a random
field, with a covariance similar to a Mat\'ern 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
Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design
An optimal experimental set-up maximizes the value of data for statistical
inferences and predictions. The efficiency of strategies for finding optimal
experimental set-ups is particularly important for experiments that are
time-consuming or expensive to perform. For instance, in the situation when the
experiments are modeled by Partial Differential Equations (PDEs), multilevel
methods have been proven to dramatically reduce the computational complexity of
their single-level counterparts when estimating expected values. For a setting
where PDEs can model experiments, we propose two multilevel methods for
estimating a popular design criterion known as the expected information gain in
simulation-based Bayesian optimal experimental design. The expected information
gain criterion is of a nested expectation form, and only a handful of
multilevel methods have been proposed for problems of such form. We propose a
Multilevel Double Loop Monte Carlo (MLDLMC), which is a multilevel strategy
with Double Loop Monte Carlo (DLMC), and a Multilevel Double Loop Stochastic
Collocation (MLDLSC), which performs a high-dimensional integration by
deterministic quadrature on sparse grids. For both methods, the Laplace
approximation is used for importance sampling that significantly reduces the
computational work of estimating inner expectations. The optimal values of the
method parameters are determined by minimizing the average computational work,
subject to satisfying the desired error tolerance. The computational
efficiencies of the methods are demonstrated by estimating the expected
information gain for Bayesian inference of the fiber orientation in composite
laminate materials from an electrical impedance tomography experiment. MLDLSC
performs better than MLDLMC when the regularity of the quantity of interest,
with respect to the additive noise and the unknown parameters, can be
exploited
A Dimension-Adaptive Multi-Index Monte Carlo Method Applied to a Model of a Heat Exchanger
We present an adaptive version of the Multi-Index Monte Carlo method,
introduced by Haji-Ali, Nobile and Tempone (2016), for simulating PDEs with
coefficients that are random fields. A classical technique for sampling from
these random fields is the Karhunen-Lo\`eve expansion. Our adaptive algorithm
is based on the adaptive algorithm used in sparse grid cubature as introduced
by Gerstner and Griebel (2003), and automatically chooses the number of terms
needed in this expansion, as well as the required spatial discretizations of
the PDE model. We apply the method to a simplified model of a heat exchanger
with random insulator material, where the stochastic characteristics are
modeled as a lognormal random field, and we show consistent computational
savings
A fully adaptive multilevel stochastic collocation strategy for solving elliptic PDEs with random data
We propose and analyse a fully adaptive strategy for solving elliptic PDEs
with random data in this work. A hierarchical sequence of adaptive mesh
refinements for the spatial approximation is combined with adaptive anisotropic
sparse Smolyak grids in the stochastic space in such a way as to minimize the
computational cost. The novel aspect of our strategy is that the hierarchy of
spatial approximations is sample dependent so that the computational effort at
each collocation point can be optimised individually. We outline a rigorous
analysis for the convergence and computational complexity of the adaptive
multilevel algorithm and we provide optimal choices for error tolerances at
each level. Two numerical examples demonstrate the reliability of the error
control and the significant decrease in the complexity that arises when
compared to single level algorithms and multilevel algorithms that employ
adaptivity solely in the spatial discretisation or in the collocation
procedure.Comment: 26 pages, 7 figure
- …