Model Reduction and Approximation

Theory and Algorithms
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.
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).

Contributor: Peter Benner (Edited by), Albert Cohen (Edited by), Mario Ohlberger (Edited by), Karen Willcox (Edited by)

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