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International Conference on Parallel computing in Electrical Engineering (PARLEC)

作者： E. Cagniot J.L. Dekeyser P. Boulet T. Brandes F. Piriou G. Marques Laboratoire dhformatique Fondamentale de Lille (LIFL) USTL-Cité scientifique Villeneuve d'Ascq France Institute for Algorithms and Scientific Computing (SCAI) German National Research Center for Information Technology (GMD) Augustin Germany Laboratoire d Electrotechnique et dElectronique de Puissance (L2EP) USTL-Cité scientifique Villeneuve d'Ascq France

Numerical simulation in electrical engineering allows one to reduce development costs by predicting device performance. An accurate prediction often requires 3D models, inducing high storage capacity and CPU power needs. As computation times can be very important, parallel computers are well suited to these models. 3D simulation in electrical engineering is based on recent research work (Whitney's elements, auto-gauged formulations, discretization of the source terms) and it results in complex and irregular codes using sparse matrices, where data accesses are done via indirect addressing. We present the results of the parallelization of a 3D magnetostatic code using High Performance Fortran (HPF). This high level programming language allows a simple and efficient approach to parallel machines. It provides both easier maintenance of the code and higher software productivity for electrical engineers.

关键词： Magnetostatics Electrical engineering Concurrent computing Power engineering computing Numerical simulation Costs Predictive models Central Processing Unit Computational modeling Computer simulation

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From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

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EPMA Journal 2014年第1期5卷 1-1页

作者： Erfan Younesi Martin Hofmann-Apitius Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven Sankt Augustin Germany Rheinische Friedrich-Wilhelms-Universität Bonn Bonn-Aachen International Center for IT Bonn Germany

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SYNTHETIC DATA GENERATION FOR A LONGITUDINAL COHORT STUDY - EVALUATION, METHOD EXTENSION AND REPRODUCTION OF PUBLISHED DATA ANALYSIS RESULTS

arXiv

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arXiv 2023年

作者： Kühnel, Lisa Schneider, Julian Perrar, Ines Adams, Tim Prasser, Fabian Nöthlings, Ute Fröhlich, Holger Fluck, Juliane Knowledge Management Zb Med Information Centre for Life Sciences Cologne50931 Germany Faculty of Technology Bielefeld University Bielefeld33615 Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn53115 Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Scai Sankt Augustin53757 Germany Medical Informatics Group Berlin Institute of Health Charité - Universitätsmedizin Berlin Berlin10117 Germany Bonn-Aachen International Center for It University of Bonn Friedrich Hirzebruch-Allee 6 Bonn53115 Germany The Agricultural Faculty University of Bonn Bonn53115 Germany

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case. © 2023, CC BY.

关键词： Large dataset

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Sustainability nexus AID: infrastructure resilience

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Sustainability Nexus Forum 2024年第1期32卷 1-7页

作者： Erfani, Tohid Naeini, Mohammad Mortazavi Borgomeo, Edoardo Anvari, Mehrnaz Hurford, Anthony Erfani, Rasool Zarei, Azin Matin, Mir A. Madani, Kaveh Department of Civil Environmental and Geomatic Engineering University College London London UK Department of Planning Industry and Environment Parramatta Australia Department of Engineering University of Cambridge Cambridge UK Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Environment Agency Wales UK Department of Engineering Manchester Metropolitan University Manchester UK United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) Dresden Germany United Nations University Institute for Water Environment and Health (UNU-INWEH) Richmond Hill Canada

Infrastructure resilience advanced through nexus thinking is pivotal for societies to handle disruptions and ensure sustainable functionality. This interconnected approach understands infrastructure as an interdependent complex system and enables cooperative planning to achieve resilience. However, challenges like data inadequacy, financial limitations and governance issues impede its adoption, especially in developing regions. The United Nations University (UNU) Sustainability Nexus Analytics, Informatics and Data (AID) Programme strives to promote integrated resource management for sustainable development and fulfilling the UN 2030 Agenda. Through its Infrastructure Resilience Module, the initiative provides tools, data platforms and localised capacity building to empower professionals and communities for evidence-based, collaborative decision-making accounting for intersectoral relationships. By supporting context-specific analytical capabilities, bridging data gaps, and governance silos, the programme aims to pave the way for resilient and sustainable infrastructure development, particularly across vulnerable regions in the Global South, which face disproportionate infrastructure service disruptions.

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A knowledge-based surrogate modeling approach for cup drawing with limited data

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IOP Conference Series: Materials Science and Engineering 2019年第1期651卷

作者： L Morand D Helm R Iza-Teran J Garcke Fraunhofer Institute for Mechanics of Materials IWM Wöhlerstraße 11 Freiburg 79108 Germany Fraunhofer Center for Machine Learning and Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin 53754 Germany Institute for Numerical Simulations University of Bonn Endenicher Allee 19b Bonn 53115 Germany

To predict the quality of a process outcome with given process parameters in real-time, surrogate models are often adopted. A surrogate model is a statistical model that interpolates between data points obtained either by process measurements or deterministic models of the process. However, in manufacturing processes the amount of useful data is often limited, and therefore setting up a sufficiently accurate surrogate model is challenging. The present contribution shows how to handle limited data in a surrogate modeling approach using the example of a cup drawing process. The purpose of the surrogate model is to classify the quality of the drawn cup and to predict its final geometry. These classification and regression tasks are solved via machine learning methods. The training data is sampled on a relatively wide range varying three parameters of a finite element simulation, namely sheet metal thickness, blank holder force, and friction. The geometrical features of the cup are extracted using domain knowledge. Besides this knowledge-based approach, an outlook is given for a data-driven surrogate modeling approach.

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INTERATOMIC-POTENTIAL-FREE, DATA-DRIVEN MOLECULAR DYNAMICS

arXiv

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arXiv 2022年

作者： Bulin, J. Hamaekers, J. Ariza, M. Pilar Ortiz, M. Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany Escuela Técnica Superior de Ingeniería Universidad de Sevilla Sevilla41092 Spain Hausdorff Center for Mathematics Universität Bonn Endenicher Allee 60 Bonn53115 Germany Division of Engineering and Applied Science California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States

We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e. g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i. e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

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FETI and Neumann-Neumann iterative substructuring methods: Connections and new results

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Communications on Pure and Applied Mathematics 2000年第1期54卷

作者： Axel Klawonn Olof Widlund GMD—German National Research Center for Information Technology SCAI—Institute for Algorithms and Scientific Computing Schloss Birlinghoven D-53754 Sankt Augustin Germany Courant Institute 251 Mercer Street New York NY 10012

The FETI and Neumann-Neumann families of algorithms are among the best known and most severely tested domain decomposition methods for elliptic partial differential equations. They are iterative substructuring methods and have many algorithmic components in common, but there are also differences. The purpose of this paper is to further unify the theory for these two families of methods and to introduce a new family of FETI algorithms. Bounds on the rate of convergence, which are uniform with respect to the coefficients of a family of elliptic problems with heterogeneous coefficients, are established for these new algorithms. The theory for a variant of the Neumann-Neumann algorithm is also redeveloped stressing similarities to that for the FETI methods. © 2001 John Wiley & Sons, Inc.

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DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

DARE: A Reflective Platform Designed to Enable Agile Data-Dr...

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IEEE International Conference on e-Science and Grid computing

作者： Iraklis Klampanos Athanasios Davvetas André Gemünd Malcolm Atkinson Antonios Koukourikos Rosa Filgueira Amrey Krause Alessandro Spinuso Angelos Charalambidis Federica Magnoni Emanuele Casarotti Christian Pagé Mike Lindner Andreas Ikonomopoulos Vangelis Karkaletsis Institute of Informatics and Telecommunications NCSR "Demokritos" Greece Fraunhofer Institute for Algorithms and Scientific Computing School of Informatics The University of Edinburgh UK NCSR "Demokritos" EPCC The University of Edinburgh UK Koninklijk Nederlands Meteorologisch Instituut (KNMI) The Netherlands Istituto Nazionale Geofisica e Vulcanologia (INGV) Italy Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Karlsruher Institut f??r Technologie (KIT) Germany Institute of Nuclear & Radiological Sciences and Technology Energy & Safety NCSR "Demokritos" Greece

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

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On the Trade-Off between Fidelity, Utility and Privacy of Synthetic Patient Data

SSRN

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SSRN 2024年

作者： Adams, Tim Birkenbihl, Colin Otte, Karen Ng, Hwei Geok Rieling, Jonas Adrian Näher, Anatol-Fiete Sax, Ulrich Prasser, Fabian Fröhlich, Holger Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany Berlin Germany Department of Medical Informatics University Medical Center Goettingen Goettingen Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Berlin Germany Bonn-Aachen International Center for IT Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Neurology Massachusetts General Hospital Harvard Medical School Boston United States

The advancement of medical research and healthcare is increasingly dependent on the analysis of patient-level data, but privacy concerns and legal constraints often hinder data sharing. Synthetic data mimicking real patient data offers a widely discussed potential solution. According to the literature, synthetic data may, however, not fully guarantee patient privacy and can vary greatly in terms of fidelity and utility. In this study, we aim to systematically investigate the trade-off between privacy, fidelity and utility of synthetic patient data. We assess synthetic data fidelity in terms of statistical similarity to real data, and utility via the performance of machine learning models trained on synthetic and tested on real data. Regarding data privacy we focus on membership inference via shadow model attacks as well as singling out and attribute inference risks. In this regard, we also consider differential privacy (DP) as a possible mechanism to probabilistically guarantee a certain level of data privacy, and we compare against classical anonymization techniques. We evaluate the fidelity, utility and privacy of synthetic data generated by five different models for three distinctive patient-level datasets. Our results show that our implementations of DP have a strongly detrimental effect on the fidelity of synthetic data, specifically its correlation structure, and therefore emphasize the need to improve techniques that effectively balance privacy, fidelity and utility in synthetic patient data generation. © 2024, The Authors. All rights reserved.

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@neurIST - Towards a System Architecture for Advanced Disease Management through Integration of Heterogeneous Data, computing, and Complex Processing Services

@neurIST - Towards a System Architecture for Advanced Diseas...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Hariharan Rajasekaran Luigi Lo Iacono Peer Hasselmeyer Jochen Fingberg Paul Summers Siegfried Benkner Gerhard Engelbrecht Antonio Arbona Alessandro Chiarini Christoph M. Friedrich Martin Hofmann-Apitius Kai Kumpf Bob Moore Philippe Bijlenga Jimison Iavindrasana Henning Mueller Rod D. Hose Robert Dunlop Alejandro Frangi IT Research Division NEC Laboratories Europe NEC Europe Limited Sankt Augustin Germany University of Oxford Oxford UK Institute of Scientific Computing University of Technology Vienna Vienna Austria Grid Systems S. A. Palma de Mallorca Spain B3C BioComputing Competence Centre Bologna Italy Fraunhofer Institute SCAI Sankt Augustin Germany IDAC Ireland Limited Ireland University Hospitals of Geneva Geneva Switzerland University of Sheffield Sheffield UK Infermed Limited UK Information & Communications Technology Department Universitat Pompeu Fabra Barcelona Spain

This paper presents the system architecture of the @neurIST project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper describes the generic requirements of the system and presents the architecture, applications and middleware technologies used to realise the system and highlights the innovations in @neurIST.

关键词： Computer architecture Diseases Aneurysm Costs Europe Project management Medical treatment Medical diagnostic imaging National electric code Hospitals

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