A weighted reduced basis method for parabolic PDEs with random data

This work considers a weighted POD-greedy method to estimate statistical outputs parabolic PDE problems with parametrized random data. The key idea of weighted reduced basis methods is to weight the parameter-dependent error estimate according to a probability measure in the set-up of the reduced space. The error of stochastic finite element solutions is usually measured in a root mean square sense regarding their dependence on the stochastic input parameters. An orthogonal projection of a snapshot set onto a corresponding POD basis defines an optimum reduced approximation in terms of a Monte Carlo discretization of the root mean square error. The errors of a weighted POD-greedy Galerkin solution are compared against an orthogonal projection of the underlying snapshots onto a POD basis for a numerical example involving thermal conduction. In particular, it is assessed whether a weighted POD-greedy solutions is able to come significantly closer to the optimum than a non-weighted equivalent. Additionally, the performance of a weighted POD-greedy Galerkin solution is considered with respect to the mean absolute error of an adjoint-corrected functional of the reduced solution.Comment: 15 pages, 4 figure

Model Reduction for Multiscale Lithium-Ion Battery Simulation

In this contribution we are concerned with efficient model reduction for multiscale problems arising in lithium-ion battery modeling with spatially resolved porous electrodes. We present new results on the application of the reduced basis method to the resulting instationary 3D battery model that involves strong non-linearities due to Buttler-Volmer kinetics. Empirical operator interpolation is used to efficiently deal with this issue. Furthermore, we present the localized reduced basis multiscale method for parabolic problems applied to a thermal model of batteries with resolved porous electrodes. Numerical experiments are given that demonstrate the reduction capabilities of the presented approaches for these real world applications

A generalized empirical interpolation method : application of reduced basis techniques to data assimilation

In an effort to extend the classical lagrangian interpolation tools, new interpolating methods that use general interpolating functions are explored. The method analyzed in this paper, called Generalized Empirical Interpolation Method (GEIM), belongs to this class of new techniques. It generalizes the plain Empirical Interpolation Method by replacing the evaluation at interpolating points by application of a class of interpolating linear functions. The paper is divided into two parts: first, the most basic properties of GEIM (such as the well-posedness of the generalized interpolation problem that is derived) will be analyzed. On a second part, a numerical example will illustrate how GEIM, if considered from a reduced basis point of view, can be used for the real-time reconstruction of experiments by coupling data assimilation with numerical simulations in a domain decomposition framework

LIUM-CVC submissions for WMT17 multimodal translation task

This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU

Conceptos Claves en el Trabajo Comunitario

Introducción. En el marco de la investigación sobre características del Trabajo Comunitario (TC) en la ciudad de Córdoba desde la perspectiva de distintos actores, plantearemos aquí la visión de las Organizaciones Políticas Territoriales (OPT). Abordaremos algunos resultados respecto del eje Conceptos Claves en el TC, conformado por las dimensiones: participación, importancia de la participación, lo construido en común (tres sub dimensiones: lo destacable, lo que aporta y lo difícil en el construir en común) y lecturas sobre TC. Se realizarán relaciones con el eje Sentimientos y con la dimensión Relación entre TC y políticas públicas. Metodología. Estudio descriptivo-cualitativo. Muestra conformada por OPT que realicen TC-territorial. Recolección de datos desde entrevista semiestructurada. Análisis cuantitativo y cualitativo. Comparación de las características del TC y sus cambios según estudios anteriores; análisis de semejanzas y diferencias entre la OPT y otros actores. Resultados. Configuran el conjunto de conceptos claves “organización” y “lo colectivo”, destacándose como central “participación”. En lo construido en común se destaca: “El hacer cosas juntos”: nuevos sentidos, vínculos, organización, cambios, aprendizajes; y “sentidos y sentimientos de pertenencia a la comunidad”. Como dificultades: “Lograr acuerdos”, “La “participación”, “indiferencia”. Discusión. Son nombrados solo una vez, “confianza” y “cambio social”. Relacionado con el campo de los sentimientos: “lo que motiva a seguir” y “lo que desanima”, en ambos se alude al cambio. El Estado es nombrado escasamente. Aquí articularemos con la dimensión “relación entre Políticas Publicas y TC” y con las matrices política-ideológicas. Se afirma una fuerte relación entre lo construido en común y participación

Addressing data sparsity for neural machine translation between morphologically rich languages

Translating between morphologically rich languages is still challenging for current machine translation systems. In this paper, we experiment with various neural machine translation (NMT) architectures to address the data sparsity problem caused by data availability (quantity), domain shift and the languages involved (Arabic and French). We show that the Factored NMT (FNMT) model, which uses linguistically motivated factors, is able to outperform standard NMT systems using subword units by more than 1 BLEU point even when a large quantity of data is available. Our work shows the benefits of applying linguistic factors in NMT when faced with low- and high-resource conditions

Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., eccentricity) or the material parameters. While model order reduction for symmetric machines is clear and does not need special treatment, the non-symmetric setting adds additional challenges. An adaptive strategy based on proper orthogonal decomposition is developed to overcome these difficulties. Equipped with an a posteriori error estimator the obtained solution is certified. Numerical examples are presented to demonstrate the effectiveness of the proposed method

Comparison of some Reduced Representation Approximations

In the field of numerical approximation, specialists considering highly complex problems have recently proposed various ways to simplify their underlying problems. In this field, depending on the problem they were tackling and the community that are at work, different approaches have been developed with some success and have even gained some maturity, the applications can now be applied to information analysis or for numerical simulation of PDE's. At this point, a crossed analysis and effort for understanding the similarities and the differences between these approaches that found their starting points in different backgrounds is of interest. It is the purpose of this paper to contribute to this effort by comparing some constructive reduced representations of complex functions. We present here in full details the Adaptive Cross Approximation (ACA) and the Empirical Interpolation Method (EIM) together with other approaches that enter in the same category

Issues in Incremental Adaptation of Statistical MT from Human Post-edits

This work investigates a crucial aspect for the integration of MT technology into a CAT environment, that is the ability of MT systems to adapt from the user feedback. In particular, weconsider the scenario of an MT system tuned for a specific translation project that after each day of work adapts from the post-edited translations created by the user. We apply and compare different state-of-the-art adaptation methods on post-edited translations generated by two professionals during two days of work with a CAT tool embedding MT suggestions. Both translators worked at the same legal document from English into Italian and German, respectively. Although exactly the same amount of translations was available each day for each language, the application of the same adaptation methods resulted in quite different out comes. This suggests that adaptation strategies should not be applied blindly, but rather taking into account language specific issues, such as data sparsity