Reduced-Order Modeling (ROM) for Simulation and Optimization

1. Front Matter

   Pages i-ix

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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

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. 

• Model Reduction
• Algorithms for scientific computing
• Scientific Computing
• Applications in Industry
• Optimization and Control
• Model order reduction
• MOR for optimization
• Model order reduction for electrochemistry

• 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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