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Wyszukujesz frazę "domain decomposition" wg kryterium: Temat

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Operator-splitting and Lagrange multiplier domain decomposition methods for numerical simulation of two coupled Navier-Stokes fluids
    Autorzy:
    Bresch, D.
    Koko, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908383.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    dwoistość
    gradient sprzężony
    przepływ Naviera-Stokesa
    domain decomposition
    duality
    conjugate gradient
    Navier-Stokes flows
    Opis:
    We present a numerical simulation of two coupled Navier-Stokes flows, using operator-splitting and optimization-based nonoverlapping domain decomposition methods. The model problem consists of two Navier-Stokes fluids coupled, through a common interface, by a nonlinear transmission condition. Numerical experiments are carried out with two coupled fluids; one with an initial linear profile and the other in rest. As expected, the transmission condition generates a recirculation within the fluid in rest.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 4; 419-429
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of patch substructuring methods
    Autorzy:
    Gander, M. J.
    Halpern, L.
    Magoules, F.
    Roux, F. X.
    Powiązania:
    https://bibliotekanauki.pl/articles/929707.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    metoda rozkładu
    metoda dopełniacza Schura
    zoptymalizowana metoda Schwarza
    Schwarz domain decomposition methods
    Schur complement methods
    patch substructuring methods
    optimized Schwarz methods
    Opis:
    Patch substructuring methods are non-overlapping domain decomposition methods like classical substructuring methods, but they use information from geometric patches reaching into neighboring subdomains, condensated on the interfaces, to enhance the performance of the method, while keeping it non-overlapping. These methods are very convenient to use in practice, but their convergence properties have not been studied yet. We analyze geometric patch substructuring methods for the special case of one patch per interface. We show that this method is equivalent to an overlapping Schwarz method using Neumann transmission conditions. This equivalence is obtained by first studying a new, algebraic patch method, which is equivalent to the classical Schwarz method with Dirichlet transmission conditions and an overlap corresponding to the size of the patches. Our results motivate a new method, the Robin patch method, which is a linear combination of the algebraic and the geometric one, and can be interpreted as an optimized Schwarz method with Robin transmission conditions. This new method has a significantly faster convergence rate than both the algebraic and the geometric one. We complement our results by numerical experiments.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2007, 17, 3; 395-402
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Finding robust transfer features for unsupervised domain adaptation
    Autorzy:
    Gao, Depeng
    Wu, Rui
    Liu, Jiafeng
    Fan, Xiaopeng
    Tang, Xianglong
    Powiązania:
    https://bibliotekanauki.pl/articles/331356.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    unsupervised domain adaptation
    feature reduction
    generalized eigenvalue decomposition
    object recognition
    adaptacja domeny
    redukcja cech
    rozkład wartości własnych
    rozpoznawanie obiektu
    Opis:
    An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 99-112
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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