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Reduced-Order Modeling (ROM) for Simulation and Optimization

Powerful Algorithms as Key Enablers for Scientific Computing

  • Book
  • © 2018

Overview

Editors:
  1. Winfried Keiper

    1. Department of Corporate Research, Robert Bosch GmbH, Renningen, Germany

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  2. Anja Milde

    1. Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany

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    You can also search for this editor in PubMed   Google Scholar

  3. Stefan Volkwein

    1. Fachbereich Mathematik, Universität Konstanz, Konstanz, Germany

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  • Addresses the advancement of efficient numerical procedures

  • Includes out-of-the-box algorithmic solutions, e.g. reduced basis methods for industrial problems

  • A special chapter on next generation Flash prepares readers for the future

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. An Iterative Model Reduction Scheme for Quadratic-Bilinear Descriptor Systems with an Application to Navier–Stokes Equations

    • Peter Benner, Pawan Goyal
    Pages 1-19

  3. Greedy Kernel Approximation for Sparse Surrogate Modeling

    • Bernard Haasdonk, Gabriele Santin
    Pages 21-45

  4. Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition

    • Dennis Beermann, Michael Dellnitz, Sebastian Peitz, Stefan Volkwein
    Pages 47-72

  5. Sequential Reduced-Order Modeling for Time-Dependent Optimization Problems with Initial Value Controls

    • Matthias Heinkenschloss, Dörte Jando
    Pages 73-98

  6. A Direct Index 1 DAE Model of Gas Networks

    • Peter Benner, Maike Braukmüller, Sara Grundel
    Pages 99-119

  7. Model Order Reduction for Rotating Electrical Machines

    • Zeger Bontinck, Oliver Lass, Oliver Rain, Sebastian Schöps
    Pages 121-140

  8. Morembs—A Model Order Reduction Package for Elastic Multibody Systems and Beyond

    • Jörg Fehr, Dennis Grunert, Philip Holzwarth, Benjamin Fröhlich, Nadine Walker, Peter Eberhard
    Pages 141-166

  9. Model Order Reduction a Key Technology for Digital Twins

    • Dirk Hartmann, Matthias Herz, Utz Wever
    Pages 167-179
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Keywords

About this book

This edited monograph collects research contributions and addresses the advancement of efficient numerical procedures in the area of model order reduction (MOR) for simulation, optimization and control. The topical scope includes, but is not limited to, new out-of-the-box algorithmic solutions for scientific computing, e.g. reduced basis methods for industrial problems and MOR approaches for electrochemical processes. The target audience comprises research experts and practitioners in the field of simulation, optimization and control, but the book may also be beneficial for graduate students alike. 

Editors and Affiliations

  • Department of Corporate Research, Robert Bosch GmbH, Renningen, Germany

    Winfried Keiper

  • Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany

    Anja Milde

  • Fachbereich Mathematik, Universität Konstanz, Konstanz, Germany

    Stefan Volkwein

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