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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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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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Ten new insights in climate science 2024

One Earth

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One Earth 2025年

作者： Schaeffer, Roberto Schipper, E. Lisa F. Ospina, Daniel Mirazo, Paula Alencar, Ane Anvari, Mehrnaz Artaxo, Paulo Biresselioglu, Mehmet Efe Blome, Tanja Boeckmann, Melanie Brink, Ebba Broadgate, Wendy Bustamante, Mercedes Cai, Wenju Canadell, Josep G. Cardinale, Roberto Chidichimo, Maria Paz Ditlevsen, Peter Eicker, Ursula Feron, Sarah Fikru, Mahelet G. Fuss, Sabine Gaye, Amadou T. Gustafsson, Örjan Harring, Niklas He, Cheng Hebden, Sophie Heilemann, Adrian Hirota, Marina Janardhanan, Nandakumar Juhola, Sirkku Jung, Tae Yong Kejun, Jiang Kilkiș, Şiir Kumarasinghe, Nilushi Lapola, David Lee, June-Yi Levis, Carolina Lusambili, Adelaide Maasakkers, Joannes D. MacIntosh, Claire Mahmood, Jemilah Mankin, Justin S. Marchegiani, Pía Martin, Maria Mukherji, Aditi Muñoz-Erickson, Tischa A. Niazi, Zeenat Nyangon, Joseph Pandipati, Santosh Perera, Amarasinghage T.D. Persad, Geeta Persson, Åsa Redman, Aaron Riipinen, Ilona Rockström, Johan Roffe, Sarah Roy, Joyashree Sakschewski, Boris Samset, Bjørn H. Schlosser, Peter Sharifi, Ayyoob Shih, Wan-Yu Sioen, Giles B. Sokona, Youba Stammer, Detlef Suk, Sunhee Thiam, Djiby Thompson, Vikki Tullos, Erin van Westen, René M. Vargas Falla, Ana Maria Vecellio, Daniel J. Worden, John Wu, Henry C. Xu, Chi Yang, Yang Zachariah, Mariam Zhang, Zhen Ziervogel, Gina Federal University of Rio de Janeiro Rio de Janeiro Brazil University of Bonn Bonn Germany Future Earth Secretariat Stockholm Sweden Arizona State University Tempe AZ United States Amazon Environmental Research Institute (IPAM) Brasilia Brazil Fraunhofer Institute for Algorithms and Scientific Computing SCAI St. Augustin Germany Potsdam Institute for Climate Impact Research (PIK) Potsdam Germany University of São Paulo São Paulo Brazil İzmir University of Economics İzmir Turkey Climate Service Center Germany (GERICS) Helmholtz-Zentrum Hereon Hamburg Germany University of Bremen Bremen Germany Lund University Lund Sweden University of Brasilia Brasilia Brazil Ocean University of China Qingdao China Xiamen University Xiamen China Commonwealth Scientific and Industrial Research Organisation (CSIRO) Environment Canberra ACT Australia University College London London United Kingdom National Scientific and Technical Research Council (CONICET) Buenos Aires Argentina National University of San Martin Buenos Aires Argentina University of Copenhagen Copenhagen Denmark Concordia University Montreal QC Canada University of Groningen Groningen Netherlands Missouri University of Science and Technology Rolla MO United States Mercator Research Institute on Global Commons and Climate Change (MCC) Berlin Germany Humboldt-University of Berlin Berlin Germany Cheikh Anta Diop University Dakar Senegal Stockholm University Stockholm Sweden University of Gothenburg Gothenburg Sweden Institute of Epidemiology Helmholtz Zentrum München—German Research Center for Environmental Health (GmbH) Neuherberg Germany European Space Agency (ESA) - European Centre for Space Applications and Telecommunications (ECSAT) Oxford United Kingdom Federal University of Santa Catarina Florianopolis Brazil Institute for Global Environmental Strategies (IGES) Hayama Japan University of Helsinki Helsinki Finland Yonsei University Graduate Seoul South Korea Chinese Academy of Macroeconomic

The years 2023 and 2024 were characterized by unprecedented warming across the globe, underscoring the urgency of climate action. Robust science advice for decision makers on subjects as complex as climate change requires deep cross- and interdisciplinary understanding. However, navigating the ever-expanding and diverse peer-reviewed literature on climate change is enormously challenging for individual researchers. We elicited expert input through an online questionnaire (188 respondents from 45 countries) and prioritized 10 key advances in climate-change research with high policy relevance. The insights span a wide range of areas, from changes in methane and aerosol emissions to the factors shaping citizens’ acceptance of climate policies. This synthesis and communications effort forms the basis for a science-policy report distributed to party delegations ahead of the 29th session of the Conference of the Parties (COP29) to inform their positions and arguments on critical issues, including heat-adaptation planning, comprehensive mitigation strategies, and strengthened governance in energy-transition minerals value chains. © 2025 The Authors

关键词： adaptation climate policy climate science governance just transition mitigation resilience science policy

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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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Orbital-enriched flat-top partition of unity method for the Schrödinger eigenproblem

arXiv

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arXiv 2018年

作者： Albrecht, Clelia Klaar, Constanze Pask, John Ernest Schweitzer, Marc Alexander Sukumar, N. Ziegenhagel, Albert Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany Physics Division Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94550 United States Institute for Numerical Simulation Rheinische Friedrich-Wilhelms-Universität Wegelerstr. 6 Bonn53115 Germany Department of Civil and Environmental Engineering University of California DavisCA95616 United States

Quantum mechanical calculations require the repeated solution of a Schrödinger equation for the wavefunctions of the system, from which materials properties follow. Recent work has shown the effectiveness of enriched finite element type Galerkin methods at significantly reducing the degrees of freedom required to obtain accurate solutions. However, time to solution has been adversely affected by the need to solve a generalized rather than standard eigenvalue problem and the ill-conditioning of associated systems matrices. In this work, we address both issues by proposing a stable and efficient orbital-enriched partition of unity method to solve the Schrödinger boundary-value problem in a parallelepiped unit cell subject to Bloch-periodic boundary conditions. In the proposed partition of unity method, the three-dimensional domain is covered by overlapping patches, with a compactly-supported weight function associated with each patch. A key ingredient in our approach is the use of non-negative weight functions that possess the flat-top property, i.e., each weight function is identically equal to unity over some finite subset of its support. This flattop property provides a pathway to devise a stable approximation over the whole domain. On each patch, we use p-th degree orthogonal (Legendre) polynomials that ensure p-th order completeness, and in addition include eigenfunctions of the radial Schrödinger equation. Furthermore, we adopt a variational lumping approach to construct a (block-)diagonal overlap matrix that yields a standard eigenvalue problem for which there exist efficient eigensolvers. The accuracy, stability, and efficiency of the proposed method is demonstrated for the Schrödinger equation with a harmonic potential as well as a localized Gaussian potential. We show that the proposed approach delivers optimal rates of convergence in the energy, and the use of orbital enrichment significantly reduces the number of degrees of freedom for a given desired accu

关键词： Eigenvalues and eigenfunctions

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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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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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A representer theorem for deep kernel learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2019年第1期20卷

作者： Bastian Bohn Christian Rieger Michael Griebel Institute for Numerical Simulation University of Bonn Bonn Germany Institute for Numerical Simulation University of Bonn Bonn Germany and Fraunhofer Center for Machine Learning Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

In this paper we provide a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces. These results serve as mathematical foundation for the analysis of machine learning algorithms based on compositions of functions. As a direct consequence in the finite-sample case, the corresponding infinite-dimensional minimization problems can be recast into (nonlinear) finite-dimensional minimization problems, which can be tackled with nonlinear optimization algorithms. Moreover, we show how concatenated machine learning problems can be reformulated as neural networks and how our representer theorem applies to a broad class of state-of-the-art deep learning methods.

关键词： artificial neural networks deep kernel learning multilayer kernel regularized least-squares regression representer theorem

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VERCE Delivers a Productive E-science Environment for Seismology Research

VERCE Delivers a Productive E-science Environment for Seismo...

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IEEE International Conference on e-Science and Grid computing

作者： Malcolm Atkinson Michele Carpené Emanuele Casarotti Steffen Claus Rosa Filgueira Anton Frank Michelle Galea Tom Garth André Gemünd Heiner Igel Iraklis Klampanos Amrey Krause Lion Krischer Siew Hoon Leong Federica Magnoni Jonas Matser Alberto Michelini Andreas Rietbrock Horst Schwichtenberg Alessandro Spinuso Jean-Pierre Vilotte University of Edinburgh UK Supercomputing Applications and Innovation Department (CINECA) Italy Istituto Nazionale di Geofisica e Vulcanologia (INGV) Italy Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Germany The University of Edinburgh Edinburgh Edinburgh GB Leibniz Supercomputing Centre Germany University of Liverpool UK Ludwig-Maximilians-University Germany Royal Netherlands Meteorological Institute (KNMI) The Netherlands Institut de Physique du Globe de Paris (IPGP) France

The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.

关键词： Computational modeling Data models Logic gates Context Earthquakes Analytical models Libraries

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NFDI4Health workflow and service for synthetic data generation, assessment and risk management

arXiv

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arXiv 2024年

作者： Moazemi, Sobhan Adams, Tim Ng, Hwei Geok Kühnel, Lisa Schneider, Julian Näher, Anatol-Fiete Fluck, Juliane Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany ZB MED – Information Centre for Life Sciences Germany Faculty of Technology Bielefeld University Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Germany Institute of Medical Informatics Charité–Universitätsmedizin Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Germany The Agricultural Faculty University of Bonn Germany Institute for Geodesy and Geoinformation University of Bonn Germany University of Bonn Germany

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI);however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany’s National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch institute (RKI). © 2024, CC BY-SA.

关键词： Electronic health record

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