Description

Book Synopsis
This book addresses the state of the art of reduced order methods for modelling and computational reduction of complex parametrised systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in various fields.

Consisting of four contributions presented at the CIME summer school, the book presents several points of view and techniques to solve demanding problems of increasing complexity. The focus is on theoretical investigation and applicative algorithm development for reduction in the complexity – the dimension, the degrees of freedom, the data – arising in these models.

The book is addressed to graduate students, young researchers and people interested in the field. It is a good companion for graduate/doctoral classes.

Table of Contents
- 1. The Reduced Basis Method in Space and Time: Challenges, Limits and Perspectives. - 2. Inverse Problems: A Deterministic Approach Using Physics-Based Reduced Models. - 3. Model Order Reduction for Optimal Control Problems. - 4. Machine Learning Methods for Reduced Order Modeling.

Model Order Reduction and Applications: Cetraro,

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    A Paperback / softback by Michael Hinze, J. Nathan Kutz, Olga Mula

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      Book details

      Publisher Springer International Publishing AG
      Published 21 June 2023
      ISBN-13 9783031295621
      978-3031295621
      ISBN-10 3031295625
      Also in:
      Mathematics Differential calculus and equations Mathematical modelling Numerical analysis

      Description

      Book Synopsis
      This book addresses the state of the art of reduced order methods for modelling and computational reduction of complex parametrised systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in various fields.

      Consisting of four contributions presented at the CIME summer school, the book presents several points of view and techniques to solve demanding problems of increasing complexity. The focus is on theoretical investigation and applicative algorithm development for reduction in the complexity – the dimension, the degrees of freedom, the data – arising in these models.

      The book is addressed to graduate students, young researchers and people interested in the field. It is a good companion for graduate/doctoral classes.

      Table of Contents
      - 1. The Reduced Basis Method in Space and Time: Challenges, Limits and Perspectives. - 2. Inverse Problems: A Deterministic Approach Using Physics-Based Reduced Models. - 3. Model Order Reduction for Optimal Control Problems. - 4. Machine Learning Methods for Reduced Order Modeling.

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