49 research outputs found
A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes
We analyze a-posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin Finite Element methods for countably-parametric elliptic boundary value problems. A residual error estimator which separates the effects of gpc-Galerkin discretization in parameter space and of the Finite Element discretization in physical space in energy norm is established. It is proved that the adaptive algorithm converges, and to this end we establish a contraction property satisfied by its iterates. It is shown that the sequences of triangulations which are produced by the algorithm in the FE discretization of the active gpc coefficients are asymptotically optimal. Numerical experiments illustrate the theoretical results
A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes
We analyze a-posteriori error estimation and adaptive refinement
algorithms for stochastic Galerkin Finite Element methods for
countably-parametric, elliptic boundary value problems. A residual error
estimator which separates the effects of gpc-Galerkin discretization in
parameter space and of the Finite Element discretization in physical space in
energy norm is established. It is proved that the adaptive algorithm
converges, and to this end we establish a contraction property satisfied by
its iterates. It is shown that the sequences of triangulations which are
produced by the algorithm in the FE discretization of the active gpc
coefficients are asymptotically optimal. Numerical experiments illustrate the
theoretical results
Adaptive stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations
Stochastic Galerkin methods for non-affine coefficient representations are
known to cause major difficulties from theoretical and numerical points of
view. In this work, an adaptive Galerkin FE method for linear parametric PDEs
with lognormal coefficients discretized in Hermite chaos polynomials is
derived. It employs problem-adapted function spaces to ensure solvability of
the variational formulation. The inherently high computational complexity of
the parametric operator is made tractable by using hierarchical tensor
representations. For this, a new tensor train format of the lognormal
coefficient is derived and verified numerically. The central novelty is the
derivation of a reliable residual-based a posteriori error estimator. This can
be regarded as a unique feature of stochastic Galerkin methods. It allows for
an adaptive algorithm to steer the refinements of the physical mesh and the
anisotropic Wiener chaos polynomial degrees. For the evaluation of the error
estimator to become feasible, a numerically efficient tensor format
discretization is developed. Benchmark examples with unbounded lognormal
coefficient fields illustrate the performance of the proposed Galerkin
discretization and the fully adaptive algorithm
Cost-optimal adaptive iterative linearized FEM for semilinear elliptic PDEs
We consider scalar semilinear elliptic PDEs where the nonlinearity is
strongly monotone, but only locally Lipschitz continuous. We formulate an
adaptive iterative linearized finite element method (AILFEM) which steers the
local mesh refinement as well as the iterative linearization of the arising
nonlinear discrete equations. To this end, we employ a damped Zarantonello
iteration so that, in each step of the algorithm, only a linear Poisson-type
equation has to be solved. We prove that the proposed AILFEM strategy
guarantees convergence with optimal rates, where rates are understood with
respect to the overall computational complexity (i.e., the computational time).
Moreover, we formulate and test an adaptive algorithm where also the damping
parameter of the Zarantonello iteration is adaptively adjusted. Numerical
experiments underline the theoretical findings