3,899 research outputs found

    Numerical Modeling For Fracture Mechanics Problems Using The Open-source Fenics Platform

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    Fracture mechanics is the mechanical approach to fracture processes, which emerged due to limitations in the application of traditional concepts of Mechanics of Materials to predict the behavior of cracked materials. Analytical problem solutions with this approach may be unattainable, which allows the use of numerical modeling, such as the finite element method. However, the use of more advanced software that solves engineering problems numerically is limited by its high cost. FEniCS is an open source computational platform that solves partial differential equations by the finite element method. Thus, from a tutorial for this computational platform, this work proposes to reproduce a classic problem of linear elastic fracture mechanics, based on the validation of a comparison of a linear elastic problem with the commercial software ANSYS ®. With the help of the provided tutorial, an code was built to model a three-point bending test. Implemented with the aid of Gmsh and Paraview, it was possible to obtain satisfactory results, and to show that FeniCS is a powerful and accessible tool for solving fracture mechanics problems. La mecánica de la fractura es el enfoque mecánico de los procesos de fractura, que surgió debido a las limitaciones en la aplicación de los conceptos tradicionales de la Mecánica de Materiales para predecir el comportamiento de los materiales fisurados. Las soluciones analíticas de los problemas con este enfoque pueden ser inalcanzables, lo que permite el uso de la modelización numérica, como el método de los elementos finitos. Sin embargo, el uso de software más avanzado que resuelve numéricamente problemas de ingeniería está limitado por su elevado coste. FEniCS es una plataforma computacional de código abierto que resuelve ecuaciones diferenciales parciales por el método de los elementos finitos. Así, a partir de un tutorial para esta plataforma computacional, este trabajo propone reproducir un problema clásico de mecánica de fractura elástica lineal, basado en la validación de una comparación de un problema elástico lineal con el software comercial ANSYS ®. Con la ayuda del tutorial proporcionado, se construyó un código para modelar un ensayo de flexión en tres puntos. Implementado con la ayuda de Gmsh y Paraview, fue posible obtener resultados satisfactorios, y demostrar que FeniCS es una herramienta potente y accesible para resolver problemas de mecánica de fractura

    Meta-learning algorithms and applications

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    Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples. Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number. Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation. More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents

    Inovação na diplomacia cultural: o caso da China

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    This study focuses on the innovation of China’s cultural diplomacy (CCD) by means of the Confucius Institute (CI). The main contents revolve around the following research goals: 1) to understand the strategic framework and practical path of CCD, and to clarify the context of its inheritance and innovation; 2) to analyze whether the CI, epitomized as a crucial innovation of CCD, has improved China’s national image in Portuguese-speaking countries (PSCs) and enhanced the attraction and international competitiveness of Chinese culture; and 3) to explore how China can better formulate its CD strategy in line with the exigencies of the modern era. The study combines the methods of literature review, case study, and questionnaire research to explore the topics from different perspectives to strengthen the scientific nature of the research results. In addition to the introduction and conclusion, the thesis is divided into five chapters. Chapter 1 discusses the connotation and value of CD. Chapter 2 expounds the development and innovation of CCD. Chapter 3 systematically summarizes China’s cultural interaction in its diplomatic process with PSCs. Chapter 4 elaborates on the CI in terms of its operation mode and diplomatic means. Chapter 5 forms the core of the study and involves empirical analysis of case-study and questionnaire data. It aims to investigate the functions, public image, influence, and practical means of CIs in the process of CD. Major findings indicate that CIs in PSCs have achieved ideal social feedback and play a positive role in shaping the image of China. However, according to the different continents where CIs are located, the survey results show distinct characteristics which are closely related to China’s different foreign policies towards Latin America, Europe, and Africa and are determined by the historical experiences and national conditions of the various countries. The future task for CCD is to clarify China’s institutional roots and the cultural genes behind its development by using cultural exchanges and China’s fluid culture to convey a message of China’s pursuit of peace, development, and cooperation.Este estudo tem como foco a inovação da Diplomacia Cultural da China (DCC) através do Instituto Confúcio (IC). Os principais conteúdos giram em torno dos seguintes objetivos de investigação: 1) compreender o enquadramento estratégico e o percurso prático da Diplomacia Cultural da China e clarificar o contexto da sua herança e inovação; 2) analisar se o IC, exemplo de inovação crucial da Diplomacia Cultural da China, melhorou a imagem nacional da China nos Países de Língua Oficial Portuguesa (PALOP) e aumentou a atração e competitividade internacional da cultura chinesa; e 3) explorar como a China pode formular melhor a sua estratégia de Diplomacia Cultural, de acordo com as exigências da era moderna. O estudo combina os métodos de revisão de literatura, estudo de caso e pesquisa de questionário para explorar os tópicos de diferentes perspetivas com o objetivo de fortalecer a natureza científica dos resultados de investigação. Além da introdução e da conclusão, a tese está dividida em cinco capítulos. O Capítulo 1 discute a conotação e o valor de Diplomacia Cultural (DC). O Capítulo 2 expõe o desenvolvimento e a inovação da Diplomacia Cultural da China (DCC). O Capítulo 3 resume sistematicamente a interação cultural da China no seu processo diplomático com os Países de Língua Oficial Portuguesa (PALOP). O Capítulo 4 discorre sobre o papel do IC em termos do seu modo de operar e dos seus meios diplomáticos. O Capítulo 5 constitui o núcleo da tese e envolve a análise empírica dos dados do estudo de caso e do questionário. Tem como objetivo investigar as funções, a imagem pública, a influência e os meios práticos dos IC no processo de Diplomacia Cultural (DC). As principais descobertas indicam que os IC nos PALOP alcançaram o feedback social ideal e desempenham um papel positivo na formação da imagem da China. No entanto, de acordo com os diferentes continentes onde os IC estão situados, os resultados da pesquisa apresentam caraterísticas distintas que estão intimamente relacionadas com as diferentes políticas externas da China para a América Latina, a Europa e a África e são determinadas pelas experiências históricas e pelas condições nacionais dos vários países. A futura tarefa da Diplomacia Cultural da China (DCC) será a de esclarecer as raízes institucionais da China e os genes culturais por trás do seu desenvolvimento, usando intercâmbios culturais e a cultura fluida da China para transmitir uma mensagem de busca de paz, de desenvolvimento e de cooperação por parte da China.Programa Doutoral em Políticas Pública

    DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method

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    This paper proposes a new easy-to-implement parameter-free gradient-based optimizer: DoWG (Distance over Weighted Gradients). We prove that DoWG is efficient -- matching the convergence rate of optimally tuned gradient descent in convex optimization up to a logarithmic factor without tuning any parameters, and universal -- automatically adapting to both smooth and nonsmooth problems. While popular algorithms following the AdaGrad framework compute a running average of the squared gradients to use for normalization, DoWG maintains a new distance-based weighted version of the running average, which is crucial to achieve the desired properties. To complement our theory, we also show empirically that DoWG trains at the edge of stability, and validate its effectiveness on practical machine learning tasks.Comment: 22 pages, 1 table, 4 figure

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    When Deep Learning Meets Polyhedral Theory: A Survey

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    In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure \unicode{x2014}such as the typical fully-connected feedforward neural network\unicode{x2014} amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks

    A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges

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    In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through reward functions (as done in reinforcement learning (RL)) has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations. This paper aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, the paper discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
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