Model Reduction and Approximation

Peter Benner is director at the Max Planck Institute for Dynamics of Complex Technical Systems and head of the Computational Methods in Systems and Control Theory department. He is also a professor at TU Chemnitz and adjunct professor at Otto-von-Guericke University Magdeburg, and he is a member of the Research Center Dynamic Systems: Systems Engineering in Magdeburg. He serves on the editorial board of several scientific journals, including SIAM Journal on Matrix Analysis and Applications. Mario Ohlberger is a full professor of applied mathematics and managing director of Applied Mathematics: Institute of Analysis and Numerics at the University of Münster. He is Associate Editor of five mathematical journals, including SIAM Journal on Scientific Computing. He is a member of the Center for Nonlinear Science, the Center for Multiscale Theory and Computation, and the Cluster of Excellence “Cells in Motion.” Albert Cohen is a professor at Laboratoire Jacques Louis Lions, Université Pierre et Marie Curie, Paris, France. He was awarded the Vasil Popov Prize (1995), the Jacques Herbrant Prize (2000), and the Blaise Pascal Prize (2004), and he has been the PI of the ERC Advanced Grant BREAD since 2014. He has been an invited speaker at ICM 2002 (Numerical Analysis section) and plenary speaker at ICIAM 2007. He is the managing editor of Foundations of Computational Mathematics. He has been a senior member of Institut Universitaire de France since 2013. Karen E. Willcox is Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology and Co-Director of the MIT Center for Computational Engineering. Prior to joining the faculty at MIT, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. She has served in multiple leadership positions within AIAA and SIAM, including on the SIAM Activity Group on Computational Science and Engineering. She is Section Editor of SIAM Journal on Scientific Computing and Associate Editor of AIAA Journal.

Preface
Part I: Sampling-Based Methods
Chapter 1: POD for Linear-Quadratic Optimal Control
Chapter 2: A Tutorial on RB-Methods
Chapter 3: The Theoretical Foundation of Reduced Basis Methods
Part II: Tensor-Based Methods
Chapter 4: Low-Rank Methods for High-Dimensional Approximation
Chapter 5: Model Reduction for High-Dimensional Parametric Problems by Tensor Techniques
Part III: System-Theoretic Methods
Chapter 6: Model Order Reduction Based on Systems Building
Chapter 7: Interpolatory Model Reduction
Chapter 8: The Loewner Framework for Model Reduction
Chapter 9: Comparison of Methods for PMOR
Index.