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Sebastian Schöps

Researcher at Technische Universität Darmstadt

Publications -  283
Citations -  1908

Sebastian Schöps is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Finite element method & Discretization. The author has an hindex of 18, co-authored 256 publications receiving 1535 citations. Previous affiliations of Sebastian Schöps include Katholieke Universiteit Leuven & University of Wuppertal.

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Journal ArticleDOI

Robust Shape Optimization of Electric Devices Based on Deterministic Optimization Methods and Finite Element Analysis With Affine Decomposition and Design Elements

Ion Gabriel Ion, +7 more
- 18 May 2018 - 
TL;DR: In this paper, gradient-based optimization methods are combined with finite-element modeling for improving electric devices, where geometric design parameters are considered by affine decomposition of the geometry or by the design element approach, both of which avoid remeshing.
Journal ArticleDOI

Aspects of Coupled Problems in Computational Electromagnetics Formulations

Markus Clemens, +6 more
- 15 Mar 2023 - 
TL;DR: Theoretical issues of accuracy, stability and numerical efficiency of the resulting formulations are addressed along with advantages and disadvantages of the various approaches in this article , including domain decomposition, multiscale problems, multiple or hybrid discrete field formulation and multi-physics problems.
Proceedings Article

Space-time discretization of Maxwell's equations in the setting of Geometric Algebra

Mariusz Klimek, +3 more
TL;DR: In this article, a discrete version of the Geometric Algebra (GA) for a Cartesian grid is investigated and is shown to be equivalent to Tonti's approach under quite natural assumptions.
Book ChapterDOI

Generalized Elements for a Structural Analysis of Circuits

Idoia Cortes Garcia, +3 more
TL;DR: In this paper, the structural analysis of circuits is expanded to systems containing such generalized elements, which may for example contain additional internal degrees of freedom, such that those elements still behave structurally like resistances, inductances and capacitances.
Journal ArticleDOI

A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

Mona Fuhrländer, +1 more
- 30 Mar 2020 - 
TL;DR: An efficient and reliable method for stochastic yield estimation using Gaussian process regression, which gives not only an approximation of the function value, but also an error indicator that can be used to decide whether a sample point should be reevaluated or not.