25 research outputs found
Randomized Local Model Order Reduction
In this paper we propose local approximation spaces for localized model order
reduction procedures such as domain decomposition and multiscale methods. Those
spaces are constructed from local solutions of the partial differential
equation (PDE) with random boundary conditions, yield an approximation that
converges provably at a nearly optimal rate, and can be generated at close to
optimal computational complexity. In many localized model order reduction
approaches like the generalized finite element method, static condensation
procedures, and the multiscale finite element method local approximation spaces
can be constructed by approximating the range of a suitably defined transfer
operator that acts on the space of local solutions of the PDE. Optimal local
approximation spaces that yield in general an exponentially convergent
approximation are given by the left singular vectors of this transfer operator
[I. Babu\v{s}ka and R. Lipton 2011, K. Smetana and A. T. Patera 2016]. However,
the direct calculation of these singular vectors is computationally very
expensive. In this paper, we propose an adaptive randomized algorithm based on
methods from randomized linear algebra [N. Halko et al. 2011], which constructs
a local reduced space approximating the range of the transfer operator and thus
the optimal local approximation spaces. The adaptive algorithm relies on a
probabilistic a posteriori error estimator for which we prove that it is both
efficient and reliable with high probability. Several numerical experiments
confirm the theoretical findings.Comment: 31 pages, 14 figures, 1 table, 1 algorith
(Parametrized) First Order Transport Equations: Realization of Optimally Stable Petrov-Galerkin Methods
We consider ultraweak variational formulations for (parametrized) linear
first order transport equations in time and/or space. Computationally feasible
pairs of optimally stable trial and test spaces are presented, starting with a
suitable test space and defining an optimal trial space by the application of
the adjoint operator. As a result, the inf-sup constant is one in the
continuous as well as in the discrete case and the computational realization is
therefore easy. In particular, regarding the latter, we avoid a stabilization
loop within the greedy algorithm when constructing reduced models within the
framework of reduced basis methods. Several numerical experiments demonstrate
the good performance of the new method
Optimal Local Approximation Spaces for Component-Based Static Condensation Procedures
In this paper we introduce local approximation spaces for component-based static condensation (sc) procedures that are optimal in the sense of Kolmogorov. To facilitate simulations for large structures such as aircraft or ships, it is crucial to decrease the number of degrees of freedom on the interfaces, or “ports,” in order to reduce the size of the statically condensed system. To derive optimal port spaces we consider a (compact) transfer operator that acts on the space of harmonic extensions on a two-component system and maps the traces on the ports that lie on the boundary of these components to the trace of the shared port. Solving the eigenproblem for the composition of the transfer operator and its adjoint yields the optimal space. For a related work in the context of the generalized finite element method, we refer the reader to [I. Babuška and R. Lipton, Multiscale Model. Simul., 9 (2011), pp. 373--406]. We further introduce a spectral greedy algorithm to generalize the procedure to the parameter-dependent setting and to construct a quasi-optimal parameter-independent port space. Moreover, it is shown that, given a certain tolerance and an upper bound for the ports in the system, the spectral greedy constructs a port space that yields an sc approximation error on a system of arbitrary configuration which is smaller than this tolerance for all parameters in a rich train set. We present our approach for isotropic linear elasticity, although the idea may be readily applied to any linear coercive problem. Numerical experiments demonstrate the very rapid and exponential convergence both of the eigenvalues and of the sc approximation based on spectral modes for nonseparable and irregular geometries such as an I-beam with an internal crack.United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Grant FA9550-09-1-0613)United States. Office of Naval Research (Grant N00014-11-1-0713
A dimensional reduction approach based on the application of reduced basis methods in the context of hierarchical model reduction
In dieser Dissertation wird vor dem Anwendungshintergrund von Grundwasserströmungen eine neue Dimensionsreduktionsmethode hergeleitet, welche reduzierte Basistechniken zur Generierung von Basisfunktionen innerhalb der hierarchischen Modellreduktionsmethode anwendet. Dabei wird entlang der dominanten Fließrichtung des betrachteten Phänomens ein Standarddiskretisierungsverfahren eingesetzt und mit optimal an das Problem angepassten hierarchischen Basisfunktionen in transversaler Richtung kombiniert. Die hierarchischen Basen werden hierbei mit reduzierte Basistechniken aus Lösungen von in der Arbeit hergeleiteten parameterabhängigen niederdimensionalen Problemen ausgewählt. In einem zweiten Schritt wird die vorgeschlagene Dimensionsreduktionsmethode weiterentwickelt um auch nichtlineare Differentialgleichungen effizient behandeln zu können. In numerischen Experimenten für lineare und nichtlineare Differentialgleichungen wird die schnelle Konvergenz und Effizienz der Methode nachgewiesen. <br/
Randomized residual-based error estimators for parametrized equations
International audienceWe propose a randomized a posteriori error estimator for reduced order approximations of parametrized (partial) differential equations. The error estimator has several important properties: the effectivity is close to unity with prescribed lower and upper bounds at specified high probability; the estimator does not require the calculation of stability (coercivity, or inf-sup) constants; the online cost to evaluate the a posteriori error estimator is commensurate with the cost to find the reduced order approximation; the probabilistic bounds extend to many queries with only modest increase in cost. To build this estimator, we first estimate the norm of the error with a Monte-Carlo estimator using Gaussian random vectors whose covariance is chosen according to the desired error measure, e.g. user-defined norms or quantity of interest. Then, we introduce a dual problem with random right-hand side the solution of which allows us to rewrite the error estimator in terms of the residual of the original equation. In order to have a fast-to-evaluate estimator, model order reduction methods can be used to approximate the random dual solutions. Here, we propose a greedy algorithm that is guided by a scalar quantity of interest depending on the error estimator. Numerical experiments on a multi-parametric Helmholtz problem demonstrate that this strategy yields rather low-dimensional reduced dual spaces
Localized model reduction for parameterized problems
In this contribution we present a survey of concepts in localized model order
reduction methods for parameterized partial differential equations. The key
concept of localized model order reduction is to construct local reduced spaces
that have only support on part of the domain and compute a global approximation
by a suitable coupling of the local spaces. In detail, we show how optimal
local approximation spaces can be constructed and approximated by random
sampling. An overview of possible conforming and non-conforming couplings of
the local spaces is provided and corresponding localized a posteriori error
estimates are derived. We introduce concepts of local basis enrichment, which
includes a discussion of adaptivity. Implementational aspects of localized
model reduction methods are addressed. Finally, we illustrate the presented
concepts for multiscale, linear elasticity and fluid-flow problems, providing
several numerical experiments.
This work has been accepted as a chapter in P. Benner, S. Grivet-Talocia, A.
Quarteroni, G. Rozza, W.H.A. Schilders, L.M. Sileira. Handbook on Model Order
Reduction. Walter De Gruyter GmbH, Berlin, 2019+