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Institution

Bauhaus University, Weimar

EducationWeimar, Thüringen, Germany
About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.


Papers
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Journal ArticleDOI
TL;DR: A sensitivity analysis toolbox consisting of a set of Matlab functions that offer utilities for quantifying the influence of uncertain input parameters on uncertain model outputs is provided.

490 citations

Journal ArticleDOI
TL;DR: A method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy that increases the robustness of the neural network approximation and can result in significant computational savings, particularly when the solution is non-smooth.
Abstract: We present a method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy. In this procedure, a coarse grid of training points is used at the initial training stages, while more points are added at later stages based on the value of the residual at a larger set of evaluation points. This method increases the robustness of the neural network approximation and can result in significant computational savings, particularly when the solution is non-smooth. Numerical results are presented for benchmark problems for scalar-valued PDEs, namely Poisson and Helmholtz equations, as well as for an inverse acoustics problem.

450 citations

Journal ArticleDOI
TL;DR: Shape memory alloys (SMAs) as discussed by the authors are metallic materials with great potential to enhance civil engineering structures, such as damping, active vibration control and prestressing or posttensioning of structures with fibres and tendons.
Abstract: Shape memory alloys (SMAs) are metallic materials with great potential to enhance civil engineering structures. They are often referred to as smart materials. A basic description of their highly non-linear material behaviour in terms of shape memory effect, superelasticity, martensite damping and variable stiffness is given in this article. It is followed by a brief introduction to Ni−Ti and Fe−Mn−Si SMAs. Pre-existing and new applications in the fields of damping, active vibration control and prestressing or posttensioning of structures with fibres and tendons are being reviewed with regard to civil engineering. Furthermore, the relatively high costs and the problem of retaining posttensioning forces when using some types of SMAs are named. In this regard is Fe−Mn−Si−Cr discussed as potential low cost SMA. A simple model for calculating the activation times of resistive heated SMA actuators or springs is presented. The results and measured data lead to further constrictions. Finally, new ideas for using SMAs in civil engineering structures are proposed in this article such as an improved concept for the active confinement of concrete members. This article is to introduce civil engineers to the world of shape memory alloys and invite them to contribute to their wider use in civil engineering structures.

439 citations

Posted Content
TL;DR: It is revealed that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream, and applications of the results include partisanship detection and pre-screening for semi-automatic fake news detection.
Abstract: This paper reports on a writing style analysis of hyperpartisan (i.e., extremely one-sided) news in connection to fake news. It presents a large corpus of 1,627 articles that were manually fact-checked by professional journalists from BuzzFeed. The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing. In sum, the corpus contains 299 fake news, 97% of which originated from hyperpartisan publishers. We propose and demonstrate a new way of assessing style similarity between text categories via Unmasking---a meta-learning approach originally devised for authorship verification---, revealing that the style of left-wing and right-wing news have a lot more in common than any of the two have with the mainstream. Furthermore, we show that hyperpartisan news can be discriminated well by its style from the mainstream (F1=0.78), as can be satire from both (F1=0.81). Unsurprisingly, style-based fake news detection does not live up to scratch (F1=0.46). Nevertheless, the former results are important to implement pre-screening for fake news detectors.

375 citations

Journal ArticleDOI
TL;DR: In this paper, a method for treating fluid-structure interaction of fracturing structures under impulsive loads is described, which does not require any modifications when the structure fails and allows fluid to flow through openings between crack surfaces.
Abstract: A method for treating fluid-structure interaction of fracturing structures under impulsive loads is described. The coupling method is simple and does not require any modifications when the structure fails and allows fluid to flow through openings between crack surfaces. Both the fluid and the structure are treated by meshfree methods. For the structure, a Kirchhoff-Love shell theory is adopted and the cracks are treated by introducing either discrete (cracking particle method) or continuous (partition of unity-based method) discontinuities into the approximation. Coupling is realized by a master-slave scheme where the structure is slave to the fluid. The method is aimed at problems with high-pressure and low-velocity fluids, and is illustrated by the simulation of three problems involving fracturing cylindrical shells coupled with fluids.

362 citations


Authors

Showing all 1443 results

NameH-indexPapersCitations
Timon Rabczuk9972735893
Adri C. T. van Duin7948926911
Paolo Rosso5654112757
Xiaoying Zhuang5427110082
Benno Stein533409880
Jin-Wu Jiang521757661
Gordon Wetzstein512589793
Goangseup Zi451538411
Bohayra Mortazavi441625802
Thorsten Hennig-Thurau4412317542
Jörg Hoffmann402007785
Martin Potthast401906563
Pedro M. A. Areias381075908
Amir Mosavi384326209
Guido De Roeck382748063
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202321
202260
2021224
2020249
2019247
2018273