17 research outputs found
Two-Level discretization techniques for ground state computations of Bose-Einstein condensates
This work presents a new methodology for computing ground states of
Bose-Einstein condensates based on finite element discretizations on two
different scales of numerical resolution. In a pre-processing step, a
low-dimensional (coarse) generalized finite element space is constructed. It is
based on a local orthogonal decomposition and exhibits high approximation
properties. The non-linear eigenvalue problem that characterizes the ground
state is solved by some suitable iterative solver exclusively in this
low-dimensional space, without loss of accuracy when compared with the solution
of the full fine scale problem. The pre-processing step is independent of the
types and numbers of bosons. A post-processing step further improves the
accuracy of the method. We present rigorous a priori error estimates that
predict convergence rates H^3 for the ground state eigenfunction and H^4 for
the corresponding eigenvalue without pre-asymptotic effects; H being the coarse
scale discretization parameter. Numerical experiments indicate that these high
rates may still be pessimistic.Comment: Accepted for publication in SIAM J. Numer. Anal., 201
Variational Multiscale Stabilization and the Exponential Decay of Fine-scale Correctors
This paper addresses the variational multiscale stabilization of standard
finite element methods for linear partial differential equations that exhibit
multiscale features. The stabilization is of Petrov-Galerkin type with a
standard finite element trial space and a problem-dependent test space based on
pre-computed fine-scale correctors. The exponential decay of these correctors
and their localisation to local cell problems is rigorously justified. The
stabilization eliminates scale-dependent pre-asymptotic effects as they appear
for standard finite element discretizations of highly oscillatory problems,
e.g., the poor approximation in homogenization problems or the pollution
effect in high-frequency acoustic scattering
The Finite Element Method for the time-dependent Gross-Pitaevskii equation with angular momentum rotation
We consider the time-dependent Gross-Pitaevskii equation describing the
dynamics of rotating Bose-Einstein condensates and its discretization with the
finite element method. We analyze a mass conserving Crank-Nicolson-type
discretization and prove corresponding a priori error estimates with respect to
the maximum norm in time and the - and energy-norm in space. The estimates
show that we obtain optimal convergence rates under the assumption of
additional regularity for the solution to the Gross-Pitaevskii equation. We
demonstrate the performance of the method in numerical experiments
A reduced basis localized orthogonal decomposition
In this work we combine the framework of the Reduced Basis method (RB) with
the framework of the Localized Orthogonal Decomposition (LOD) in order to solve
parametrized elliptic multiscale problems. The idea of the LOD is to split a
high dimensional Finite Element space into a low dimensional space with
comparably good approximation properties and a remainder space with negligible
information. The low dimensional space is spanned by locally supported basis
functions associated with the node of a coarse mesh obtained by solving
decoupled local problems. However, for parameter dependent multiscale problems,
the local basis has to be computed repeatedly for each choice of the parameter.
To overcome this issue, we propose an RB approach to compute in an "offline"
stage LOD for suitable representative parameters. The online solution of the
multiscale problems can then be obtained in a coarse space (thanks to the LOD
decomposition) and for an arbitrary value of the parameters (thanks to a
suitable "interpolation" of the selected RB). The online RB-LOD has a basis
with local support and leads to sparse systems. Applications of the strategy to
both linear and nonlinear problems are given
Numerical homogenization for nonlinear strongly monotone problems
In this work we introduce and analyze a new multiscale method for strongly
nonlinear monotone equations in the spirit of the Localized Orthogonal
Decomposition. A problem-adapted multiscale space is constructed by solving
linear local fine-scale problems which is then used in a generalized finite
element method. The linearity of the fine-scale problems allows their
localization and, moreover, makes the method very efficient to use. The new
method gives optimal a priori error estimates up to linearization errors. The
results neither require structural assumptions on the coefficient such as
periodicity or scale separation nor higher regularity of the solution. The
effect of different linearization strategies is discussed in theory and
practice. Several numerical examples including stationary Richards equation
confirm the theory and underline the applicability of the method