19 research outputs found

    Efficient approximations of the fisher matrix in neural networks using kronecker product singular value decomposition

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    We design four novel approximations of the Fisher Information Matrix (FIM) that plays a central role in natural gradient descent methods for neural networks. The newly proposed approximations are aimed at improving Martens and Grosse’s Kronecker-factored block diagonal (KFAC) one. They rely on a direct minimization problem, the solution of which can be computed via the Kronecker product singular value decomposition technique. Experimental results on the three standard deep auto-encoder benchmarks showed that they provide more accurate approximations to the FIM. Furthermore, they outperform KFAC and state-of-the-art first-order methods in terms of optimization speed

    Improvements on physics-informed models for lithium batteries

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    The fast adoption of battery electric vehicles (BEV) has resulted in a demand for rapid technological advancements. Strategic areas undergoing this development include lithium-ion energy storage. This is inclusive of electrochemical design improvements and advanced battery management control architectures. Field objectives for these developments include but are not limited to, reductions in cell degradation, improvements in fast charging capabilities, increases in system-level energy densities, and a reduction in energy storage costs. Improvements in online predictive models provide a path for realising these objectives through informed control interactions, reduced degradation effects, and decreased vehicle costs. This thesis contributes to these developments through improvements in fast physics-informed battery models for both lithium-ion and lithium-metal batteries. The key novelty presented is the improvement of real-time, physics-based electrochemical model generation for lithium-ion batteries. A computationally informed realisation algorithm is developed and expands on the previously published realisation algorithm methods. An open-source Julia-based architecture is presented and provides a high-performance implementation while maintaining dynamic language capabilities for fast code development, and readability. A performance improvement of 21.7\% was shown over the previous discrete realisation algorithm, with an additional framework improvement of 3.51 times when compared to the previously published framework. A methodology for the creation and modification of the reduced order models via in-vehicle hardware is presented and validated through an ARM-based model generation investigation. This addition provides a versatile method for cell degradation prediction over the battery life and can provide an interface for improved prediction of cell-to-cell variations. This methodology is applied to intercalation-based NMC/graphite batteries and is both numerically and experimentally validated. A further element of novelty produced in this thesis includes advancements in lithium-metal phase-field representations through the creation of a Julia-based numerical framework optimised for high-performance predictions. This framework is then utilised as a ground truth model for the development of an autoregressive physics-informed neural solver aimed to predict lithium-metal evolution. Through the implementation of the physics-informed neural solver, a reduction in the numerical prediction time of 40.3\% compared to the underlying phase-field representation was achieved. This methodology enables fast lithium-morphology predictions for improved design space explorations, online deployment, and advancements in electrodeposition material discovery for lithium-metal batteries

    Novel Approaches for Structural Health Monitoring

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    The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

    MIMO Systems

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    In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

    Comparación de un algoritmo de bidiagonalización para su utilización en la recuperación de información

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    Este artículo presenta parte del trabajo realizado en el marco de una investigación que pretende optimizar un Sistema de Recuperación de Información, mediante la implementación y evaluación de distintos algoritmos secuenciales y paralelos para resolver eficientemente la Descomposición en Valores Singulares. Tal proceso comienza con llevar la matriz inicial a la forma bidiagonal, lo que puede consumir más del 70% del tiempo total del proceso. Por ello, como trabajo preliminar se han estudiado distintos métodos de bidiagonalización. Este trabajo se relaciona al desarrollo e implementación de un algoritmo de bidiagonalización alternativo para comparar posteriormente su comportamiento en distintas arquitecturas, en particular, las basadas en unidades de procesamiento gráfico, monoprocesadores y multiprocesadores. La experiencia de este estudio concreto ha permitido un análisis de rendimiento al ejecutar el algoritmo en cada implementación, cuando se varía el tamaño de las matrices, identificando problemas mínimos en GPU en cuanto a diferencias en la precisión de datos.Workshop: WPDP – Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informátic

    Robust and Scalable Data Representation and Analysis Leveraging Isometric Transformations and Sparsity

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    The main focus of this doctoral thesis is to study the problem of robust and scalable data representation and analysis. The success of any machine learning and signal processing framework relies on how the data is represented and analyzed. Thus, in this work, we focus on three closely related problems: (i) supervised representation learning, (ii) unsupervised representation learning, and (iii) fault tolerant data analysis. For the first task, we put forward new theoretical results on why a certain family of neural networks can become extremely deep and how we can improve this scalability property in a mathematically sound manner. We further investigate how we can employ them to generate data representations that are robust to outliers and to retrieve representative subsets of huge datasets. For the second task, we will discuss two different methods, namely compressive sensing (CS) and nonnegative matrix factorization (NMF). We show that we can employ prior knowledge, such as slow variation in time, to introduce an unsupervised learning component to the traditional CS framework and to learn better compressed representations. Furthermore, we show that prior knowledge and sparsity constraint can be used in the context of NMF, not to find sparse hidden factors, but to enforce other structures, such as piece-wise continuity. Finally, for the third task, we investigate how a data analysis framework can become robust to faulty data and faulty data processors. We employ Bayesian inference and propose a scheme that can solve the CS recovery problem in an asynchronous parallel manner. Furthermore, we show how sparsity can be used to make an optimization problem robust to faulty data measurements. The methods investigated in this work have applications in different practical problems such as resource allocation in wireless networks, source localization, image/video classification, and search engines. A detailed discussion of these practical applications will be presented for each method

    Generalized averaged Gaussian quadrature and applications

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    A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

    MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

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    Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
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