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arXiv 2023年

作者： Sindu, B.S. Hamaekers, Jan Special and Multifunctional Structures Laboratory CSIR-Structural Engineering Research Centre Taramani Tamil Nadu Chennai600113 India Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt AugustinD-53757 Germany

Epoxy polymers are used in wide range of applications. The properties and performance of epoxy polymers depend upon various factors like the type of constituents and their proportions used and other process parameters. The conventional way of developing epoxy polymers is usually labor-intensive and may not be fully efficient, which has resulted in epoxy polymers having a limited performance range due to the use of predetermined blend combinations, compositions and development parameters. Hence, in order to experiment with more design parameters, robust and easy computational techniques need to be established. To this end, we developed and analyzed in this study a new machine learning (ML) based approach to predict the mechanical properties of epoxy polymers based on their basic structural features. The results from molecular dynamics (MD) simulations have been used to derive the ML model. The salient feature of our work is that for the development of epoxy polymers based on EPON-862, several new hardeners were explored in addition to the conventionally used ones. The influence of additional parameters like the proportion of curing agent used and the extent of curing on the mechanical properties of epoxy polymers were also investigated. This method can be further extended by providing the epoxy polymer with the desired properties through knowledge of the structural characteristics of its constituents. The findings of our study can thus lead toward development of efficient design methodologies for epoxy polymeric systems. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Kernel-based stochastic collocation for the random two-phase navier-stokes equations

arXiv

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arXiv 2018年

作者： Griebel, M. Rieger, C. Zaspel, P. Institute for Numerical Simulation Bonn University Endenicher Allee 19b BonnD-53115 Fraunhofer Institute for Algorithms and Scientific Computing Scai Schloss Birlinghoven Sankt AugustinD-53754 Department of Mathematics Rwth Aachen University Schinkelstr. 2 AachenD-52062 Department of Mathematics and Computer Science University of Basel Spiegelgasse 1 Basel4051

In this work, we apply stochastic collocation methods with radial kernel basis functions for an uncertainty quantification of the random incompressible two-phase Navier-Stokes equations. Our approach is non-intrusive and we use the existing fluid dynamics solver NaSt3DGPF to solve the incompressible two-phase Navier-Stokes equation for each given realization. We are able to empirically show that the resulting kernel-based stochastic collocation is highly competitive in this setting and even outperforms some other standard methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Viscous flow

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Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

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Procedia CIRP 2021年 104卷 641-646页

作者： Peter Burggräf Johannes Wagner Benjamin Heinbach Fabian Steinberg Alejandro R. Pérez M. Lennart Schmallenbach Jochen Garcke Daniela Steffes-lai Moritz Wolter Chair of International Production Engineering and Management (IPEM) Universität Siegen Paul-Bonatz-Straße 9-11 57076 Siegen Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 1 53757 Sankt Augustin Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven 1 53757 Sankt Augustin Germany Institute for Numerical Simulation Universität Bonn Endenicher Allee 19b 53115 Bonn Germany Institute for Computer Science Universität Bonn Endenicher Allee 19a 53115 Bonn Germany

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

关键词： Machine-Learning Predictive Quality Production Quality Assurance Classification

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DARE: A reflective platform designed to enable agile data-driven research on the cloud 15

DARE: A reflective platform designed to enable agile data-dr...

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15th IEEE International Conference on eScience, eScience 2019

作者： Klampanos, Iraklis Davvetas, Athanasios Gemund, Andre Atkinson, Malcolm Koukourikos, Antonis Filgueira, Rosa Krause, Amrey Spinuso, Alessandro Charalambidis, Angelos Magnoni, Federica Casarotti, Emanuele Page, Christian Lindner, Mike Ikonomopoulos, Andreas Karkaletsis, Vangelis Institute of Informatics and Telecommunications NCSR Demokritos Greece Fraunhofer Institute for Algorithms and Scientific Computing Germany School of Informatics University of Edinburgh United Kingdom EPCC University of Edinburgh United Kingdom Netherlands Istituto Nazionale Geofisica e Vulcanologia [INGV Italy Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique [CERFACS France Karlsruher Institut fur Technologie [KIT Germany Institute of Nuclear and Radiological Sciences and Technology Energy and Safety NCSR Demokritos Greece

ISBN: (纸本)9781728124513

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. © 2019 IEEE.

关键词： Semantics

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ϵ-dimension in infinite dimensional hyperbolic cross approximation and application to parametric elliptic PDEs

arXiv

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arXiv 2017年

作者： Dũng, Dinh Griebel, Michael Huy, Vu Nhat Rieger, Christian Information Technology Institute Vietnam National University Hanoi 144 Xuan Thuy Cau Giay Hanoi Viet Nam Institute for Numerical Simulation Bonn University Wegelerstrasse 6 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing Scai Schloss Birlinghoven Sankt Augustin53754 Germany College of Science Vietnam National University Hanoi 334 Nguyen Trai Thanh Xuan Hanoi Viet Nam

In this article, we present a cost-benefit analysis of the approximation in tensor products of Hilbert spaces of Sobolev-analytic type. The Sobolev part is defined on a finite dimensional domain, whereas the analytical space is defined on an infinite dimensional domain. As main mathematical tool, we use the ϵ-dimension of a subset in a Hilbert space. The ϵ-dimension gives the lowest number of linear information that is needed to approximate an element from the set in the norm of the Hilbert space up to an accuracy ϵ > 0. From a practical point of view this means that we a priori fix an accuracy and ask for the amount of information to achieve this accuracy. Such an analysis usually requires sharp estimates on the cardinality of certain index sets which are in our case infinite-dimensional hyperbolic crosses. As main result, we obtain sharp bounds of the ϵ-dimension of the Sobolev-analytic-type function classes which depend only on the smoothness differences in the Sobolev spaces and the dimension of the finite dimensional domain where these spaces are defined. This implies in particular that, up to constants, the costs of the infinite dimensional (analytical) approximation problem is dominated by the finite-variate Sobolev approximation problem. We demonstrate this procedure with an examples of functions spaces stemming from the regularity theory of parametric partial differential equations. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Integrating Cloud computing, Bayesian Optimization, and Neural-Additive Modeling for Enhanced CAM Systems in 5-Axis Milling

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Procedia CIRP 2024年 128卷 532-537页

作者： Viktor Rudel Georg Vinogradov Philipp Ganser Thomas Bergs Christopher Vahl Markus Frings Valentina König Maximilian Schambach Stefan Dietzel Michael Königs Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 53757 Sankt Augustin Germany ModuleWorks GmbH Henricistraße 50 52072 Aachen Germany Merantix Momentum Merantix AI Campus Max-Urich-Straße 3 13355 Berlin Germany EXAPT Systemtechnik GmbH Theaterstraße 12 52062 Aachen Germany

This publication introduces the development and application of an advanced CAD/CAM/CAE system that leverages the computational capabilities of an edge-cloud infrastructure. The developed system consists of containerized technology models for various complex process planning simulation tasks in 5-axis-milling, such as toolpath calculation, cutter-workpiece-engagement, uncut chip geometry or cutting force simulations. Moreover, a Bayesian optimization (BO) algorithm is coupled with the system enabling multi-objective optimizations of the considered machining operations by varying predefined CAM-parameters. The result of the optimization consists of a set of Pareto-efficient solutions. Each solution realizes a different tradeoff between the technological objectives of the process planner. Since mastering the complexity of the design space is a major challenge in today’s CAM programming workflow, a Neural-Additive Model (NAM) is coupled with the system improving guided search through the configuration/result space. This reduces the convergence time to an optimal CAM parameter set. The coupling of the cloud computing, multi-objective optimization and artificial intelligence method with a CAM-kernel is demonstrated based on the process design of 5-axis milling operations for a blade-integrated disk (blisk).

关键词： Computer-aided Manufacturing systems (CAM-Systems) Multi-objective optimization Design methodology tools technologies

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Improving fold recognition of protein threading by experimental distance constraints

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In silico biology 2002年第3期2卷 325-37页

作者： Mario Albrecht Daniel Hanisch Ralf Zimmer Thomas Lengauer Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53 754 Sankt Augustin Germany. mario.albrecht@scai.fhg.de

We present a comprehensive analysis of methods for improving the fold recognition rate of the threading approach to protein structure prediction by the utilization of few additional distance constraints. The distance constraints between protein residues may be obtained by experiments such as mass spectrometry or NMR spectroscopy. We applied a post-filtering step with new scoring functions incorporating measures of constraint satisfaction to ranking lists of 123D threading alignments. The detailed analysis of the results on a small representative benchmark set show that the fold recognition rate can be improved significantly by up to 30% from about 54%-65% to 77%-84%, approaching the maximal attainable performance of 90% estimated by structural superposition alignments. This gain in performance adds about 10% to the recognition rate already achieved in our previous study with cross-link constraints only. Additional recent results on a larger benchmark set involving a confidence function for threading predictions also indicate notable improvements by our combined approach, which should be particularly valuable for rapid structure determination and validation of protein models.

关键词： protein threading fold recognition structure prediction experimentaldata distance constraints cross-linking reagents mass spectrometry NOE restraints NMR

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Co-clustering of biological networks and gene expression data

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Bioinformatics (Oxford, England) 2002年第SUPPL_1期18 Suppl 1卷 S145-54页

作者： Daniel Hanisch Alexander Zien Ralf Zimmer Thomas Lengauer Institute for Algorithms and Scientific Computing (SCAI) Fraunhofer Gesellschaft Schloss Birlinghoven Sankt Augustin 53754 Germany. Daniel.Hanisch@scai.fhg.de

MOTIVATION:Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS:We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

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Centralization: A new method for the normalization of gene expression data

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Bioinformatics 2001年第SUPPL_1期17卷 S323-S331页

作者： Zien, Alexander Aigner, Thomas Zimmer, Ralf Lengauer, Thomas SCAI - Institute for Algorithms and Scientific Computing GMD - German National Research Center for Information Technology Schloss Birlinghoven Sankt Augustin 53754 Germany Department of Pathology University of Erlangen-Nürnberg Erlangen 91054 Krankenhausstr. 8-10 Germany

Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed. © Oxford University Press 2001.

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Preface

Lecture Notes in Computational Science and Engineering

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Lecture Notes in Computational Science and Engineering 2017年 115卷 V页

作者： Griebel, Michael Schweitzer, Marc Alexander Institut für Numerische Simulation Universität Bonn Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

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