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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: In this paper, the authors investigated the influence of hydroxide concentration as well as Si/Al in the model system of aluminosilicate gels and showed that a gel with a preferred Si-Al ratio wants to condense.
Abstract: The reaction of geopolymer binders can be subdivided into two more or less parallel reactions, (1) the dissolution of reactable silicate and aluminate monomers from the reactive solid material and (2) the condensation to an aluminosilicate gel. Due to the wide range of possible raw materials, the question arises whether the Si/Al ratio of the hardened aluminosilicate network is predominated by the Si/Al ratio of the raw materials, or a gel with preferred Si/Al ratio wants to condense. Therefore, aluminosilicate gels were synthesized with pure alkali silicate and alkali aluminate solutions. Two measurement series were started to investigate the influence of hydroxide concentration as well as the influence of Si/Al in the model system. The gels were characterized by chemical analysis, FT-IR spectroscopy, X-ray diffraction as well as 29Si and 27Al MAS NMR spectroscopy.

89 citations

Journal ArticleDOI
TL;DR: In this article, a Taylor-type series expansion based on the Appell polynomials is presented, which can be related to the corresponding Fourier series analogously as in the complex one-dimensional case.
Abstract: Recently Appell systems of monogenic polynomials in ℝ3 were constructed by several authors Main purpose of this paper is the description of another Appell system that is complete in the space of square integrable quaternion-valued functions A new Taylor-type series expansion based on the Appell polynomials is presented, which can be related to the corresponding Fourier series analogously as in the complex one-dimensional case These results find applications in the description of the hypercomplex derivative, the monogenic primitive of a monogenic function and the characterization of functions from the monogenic Dirichlet space Copyright © 2009 John Wiley & Sons, Ltd

88 citations

Journal ArticleDOI
01 Jul 2016-Carbon
TL;DR: In this article, the authors provided a general view regarding phonon and load transfer along amorphous graphene and developed models for the evaluation of mechanical and thermal conductivity properties yield accurate results for pristine graphene and acquired findings for amorphized graphene films are size independent.

88 citations

Journal ArticleDOI
TL;DR: In this article, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been employed to predict the fracture energy of polymer nanocomposites.

88 citations

Journal ArticleDOI
TL;DR: A data-efficient learning approach that combines several techniques of machine learning and statistics for performance prediction of configurable systems is proposed, called DECART, and a sample quality metric is proposed and introduced to introduce a quantitative analysis of the quality of a sample forperformance prediction.
Abstract: Many software systems today are configurable, offering customization of functionality by feature selection. Understanding how performance varies in terms of feature selection is key for selecting appropriate configurations that meet a set of given requirements. Due to a huge configuration space and the possibly high cost of performance measurement, it is usually not feasible to explore the entire configuration space of a configurable system exhaustively. It is thus a major challenge to accurately predict performance based on a small sample of measured system variants. To address this challenge, we propose a data-efficient learning approach, called DECART, that combines several techniques of machine learning and statistics for performance prediction of configurable systems. DECART builds, validates, and determines a prediction model based on an available sample of measured system variants. Empirical results on 10 real-world configurable systems demonstrate the effectiveness and practicality of DECART. In particular, DECART achieves a prediction accuracy of 90% or higher based on a small sample, whose size is linear in the number of features. In addition, we propose a sample quality metric and introduce a quantitative analysis of the quality of a sample for performance prediction.

88 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