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Full Description
Many physical, chemical, biomedical, and technical processes can be described by partial differential equations or dynamical systems. In spite of increasing computational capacities, many problems are of such high complexity that they are solvable only with severe simplifications, and the design of efficient numerical schemes remains a central research challenge. This book presents a tutorial introduction to recent developments in mathematical methods for model reduction and approximation of complex systems.
Model Reduction and Approximation: Theory and Algorithms:
contains three parts that cover (I) sampling-based methods, such as the reduced basis method and proper orthogonal decomposition, (II) approximation of high-dimensional problems by low-rank tensor techniques, and (III) system-theoretic methods, such as balanced truncation, interpolatory methods, and the Loewner framework
is tutorial in nature, giving an accessible introduction to state-of-the-art model reduction and approximation methods; and
covers a wide range of methods drawn from typically distinct communities (sampling based, tensor based, system-theoretic).
Contents
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.
