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Model Reduction and Approximation
Theory and Algorithms
Albert Cohen , Karen Willcox , Mario Ohlberger , Peter Benner
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. It is tutorial in nature, giving an accessible introduction to state-of-the-art model reduction and approximation methods. It also covers a wide range of methods drawn from typically distinct communities (sampling based, tensor based, system-theoretic).??
This book is intended for researchers interested in model reduction and approximation, particularly graduate students and young researchers.
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