Austrian Academy of Sciences

Elektronisches Publikationsportal der Österreichischen Akademie der Wissenschaften
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    Mathematical analysis of some iterative methods for the reconstruction of memory kernels. ETNA - Electronic Transactions on Numerical Analysis

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    We analyze three iterative methods that have been proposed in the computational physics community for the reconstruction of memory kernels in a stochastic delay differential equation known as the generalized Langevin equation. These methods use the autocorrelation function of the solution of this equation as input data. Although they have been demonstrated to be useful, a straightforward Laplace analysis does not support their conjectured convergence. We provide more detailed arguments to explain the good performance of these methods in practice. In the second part of this paper we investigate the solution of the generalized Langevin equation with a perturbed memory kernel. We establish sufficient conditions including error bounds such that the stochastic process corresponding to the perturbed problem converges to the unperturbed process in the mean square sense

    Apropos: Nachhaltigkeit – eine Videoreihe rund um Konsum. GW-Unterricht|GW-Unterricht 162|

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    Im vorliegenden Beitrag stellen Studierende der Fachhochschule Salzburg Ergebnisse eines Projekts vor, im Rahmen dessen sie zielgruppenorientierte Videos zum Thema Konsum erstellt haben. Als Zielgruppe werden Jugendliche ab 14 Jahren sowie Lehrkräfte, welche dieses Material im Unterricht verwenden möchten, angesprochen. In diesen Videos werden die Schwerpunkte Overshoot Day, Konsumgeschichte, Konsumpsychologie, Nachhaltigkeit, Sustainable Development Goals, nachhaltiger Konsum und Tipps für einen bewussteren Konsum dargestellt

    Coarsening in algebraic multigrid using Gaussian processes. ETNA - Electronic Transactions on Numerical Analysis

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    Multigrid methods have proven to be an invaluable tool to efficiently solve large sparse linear systems arising in the discretization of Partial Differential Equations (PDEs). Algebraic multigrid methods and in particular adaptive algebraic multigrid approaches have shown that multigrid efficiency can be obtained without having to resort to properties of the PDE. Yet the required setup of these methods poses a not negligible overhead cost. Methods from machine learning have attracted attention to streamline processes based on statistical models being trained on the available data. Interpreting algebraically smooth error as an instance of a Gaussian process, we develop a new, data driven approach to construct adaptive algebraic multigrid methods. Based on Gaussian a priori distributions, kriging interpolation minimizes the mean squared error of the a posteriori distribution, given the data on the coarse grid. Going one step further, we exploit the quantification of uncertainty in the Gaussian process model in order to construct efficient variable splittings. Using a semivariogram fit of a suitable covariance model we demonstrate that our approach yields efficient methods using a single algebraically smooth vector

    Bahnlärm einmal anders

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    Bahnlärm endet nicht beim während der Vorbeifahrt abgestrahlten Luftschall und dem durch dieEinwirkung auf den Untergrund induzierten sekundären Luftschall. Neben den stationärenQuellen von Schienenfahrzeugen, die sich speziell in Bahnhöfen und Endstellen vonStraßenbahnen auswirken, sind besonders die Warnsignale an Eisenbahnkreuzungen, dieLautsprecherdurchsagen in Bahnhöfen und Haltestellen sowie der Baulärm und die mitBauarbeiten verbundenen Warnsignale erhebliche Quellen von Belästigungen

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    Elektronisches Publikationsportal der Österreichischen Akademie der Wissenschaften is based in Austria
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