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In many high-frequency simulation workflows, eigenvalue tracking along a parameter variation is necessary. This can become computationally prohibitive when repeated time-consuming eigenvalue problems must be solved. Therefore, we employ a…

Computational Engineering, Finance, and Science · Computer Science 2023-08-07 Max Kappesser , Anna Ziegler , Sebastian Schöps

We consider a class of parameter-dependent optimal control problems of elliptic PDEs with constraints of general type on the control variable. Applying the concept of variational discretization, [4], together with techniques from the…

Optimization and Control · Mathematics 2018-08-20 Ahmad Ahmad Ali , Michael Hinze

In this work we consider (hierarchical, Lagrange) reduced basis approximation and a posteriori error estimation for elasticity problems in affinley parametrized geometries. The essential ingredients of the methodology are: a Galerkin…

Numerical Analysis · Mathematics 2018-01-23 Dinh Bao Phuong Huynh , Federico Pichi , Gianluigi Rozza

We consider the computation of averaged coefficients for the homogenization of elliptic partial differential equations. In this problem, like in many multiscale problems, a large number of similar computations parametrized by the…

Numerical Analysis · Mathematics 2016-08-14 Sébastien Boyaval

We simulate the vibration of a violin bridge in a multi-query context using reduced basis techniques. The mathematical model is based on an eigenvalue problem for the orthotropic linear elasticity equation. In addition to the nine material…

Numerical Analysis · Mathematics 2016-06-06 Thomas Horger , Barbara Wohlmuth , Linus Wunderlich

In this article, we study eigenvalue problems associated to self-adjoint operators and their approximation obtained by subspace projection, as used in the reduced basis method for instance. We provide error bounds between the exact…

Mathematical Physics · Physics 2025-06-16 Louis Garrigue , Benjamin Stamm

We investigate a class of parametric elliptic eigenvalue problems with homogeneous essential boundary conditions where the coefficients (and hence the solution $u$) may depend on a parameter $y$. For the efficient approximate evaluation of…

Numerical Analysis · Mathematics 2024-05-17 Alexey Chernov , Tung Le

In this paper we discuss reduced order models for the approximation of parametric eigenvalue problems. In particular, we are interested in the presence of intersections or clusters of eigenvalues. The singularities originating by these…

Numerical Analysis · Mathematics 2024-07-11 Daniele Boffi , Abdul Halim , Gopal Priyadarshi

We present a reduced basis approach to solve the convected Helmholtz equation with several physical parameters. Physical parameters characterize the aeroacoustic wave propagation in terms of the wave and Mach numbers. We compute solutions…

Numerical Analysis · Mathematics 2015-06-10 Myoungnyoun Kim , Imbo Sim

In this paper, we propose a certified reduced basis (RB) method for quasilinear elliptic problems together with its application to nonlinear magnetostatics equations, where the later model permanent magnet synchronous motors (PMSM). The…

Numerical Analysis · Mathematics 2020-07-03 Michael Hinze , Denis Korolev

Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approximation for parametric linear eigenvalue problems, aiming to…

We generalize and analyse the method for computing lower bounds of the principal eigenvalue proposed in our previous paper (I. Sebestova, T. Vejchodsky, SIAM J. Numer. Anal. 2014). This method is suitable for symmetric elliptic eigenvalue…

Numerical Analysis · Mathematics 2016-06-07 Ivana Sebestova , Tomas Vejchodsky

Parametric model order reduction using reduced basis methods can be an effective tool for obtaining quickly solvable reduced order models of parametrized partial differential equation problems. With speedups that can reach several orders of…

Numerical Analysis · Mathematics 2022-01-26 Mario Ohlberger , Stephan Rave

We present a perturbed subspace iteration algorithm to approximate the lowermost eigenvalue cluster of an elliptic eigenvalue problem. As a prototype, we consider the Laplace eigenvalue problem posed in a polygonal domain. The algorithm is…

Numerical Analysis · Mathematics 2021-04-13 Stefano Giani , Luka Grubišić , Luca Heltai , Ornela Mulita

The reduced basis method is a model reduction technique yielding substantial savings of computational time when a solution to a parametrized equation has to be computed for many values of the parameter. Certification of the approximation is…

Numerical Analysis · Mathematics 2014-05-16 Fabien Casenave , Alexandre Ern , Tony Lelièvre

In this paper, to the best of our knowledge, we make the first attempt at studying the parametric semilinear elliptic eigenvalue problems with the parametric coefficient and some power-type nonlinearities. The parametric coefficient is…

Numerical Analysis · Mathematics 2024-05-02 Byeong-Ho Bahn

In this paper, we discuss adaptive approximations of an elliptic eigenvalue optimization problem in a phase-field setting by a conforming finite element method. An adaptive algorithm is proposed and implemented in several two dimensional…

Numerical Analysis · Mathematics 2025-03-10 Jing Li , Yifeng Xu , Shengfeng Zhu

In this paper the authors study a non-linear elliptic-parabolic system, which is motivated by mathematical models for lithium-ion batteries. One state satisfies a parabolic reaction diffusion equation and the other one an elliptic equation.…

Numerical Analysis · Mathematics 2023-08-02 Behzad Azmi , Andrea Petrocchi , Stefan Volkwein

We consider an elliptic PDE in two variables. As one parameter approaches zero, this PDE collapses to a parabolic one, that is forward parabolic in a part of the domain and backward parabolic in the remainder. Such problems arise naturally…

Analysis of PDEs · Mathematics 2007-05-23 Diego Dominici , Charles Knessl

A key quantity that occurs in the error analysis of several numerical methods for eigenvalue problems is the distance between the eigenvalue of interest and the next nearest eigenvalue. When we are interested in the smallest or fundamental…

Numerical Analysis · Mathematics 2024-12-20 Alexander D. Gilbert , Ivan G. Graham , Robert Scheichl , Ian H. Sloan
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