Model Order Reduction. Volume 1, System- and Data-Driven Methods and Algorithms /
An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real...
Clasificación: | Libro Electrónico |
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Otros Autores: | , , , , , , , , , , , , , , , , , |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Berlin ; Boston :
De Gruyter,
[2021]
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Colección: | Model Order Reduction ;
Volume 1 |
Temas: | |
Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Frontmatter
- Preface to the first volume of Model Order Reduction
- Contents
- 1 Model order reduction: basic concepts and notation
- 2 Balancing-related model reduction methods
- 3 Model order reduction based on moment-matching
- 4 Modal methods for reduced order modeling
- 5 Post-processing methods for passivity enforcement
- 6 The Loewner framework for system identification and reduction
- 7 Manifold interpolation
- 8 Vector fitting
- 9 Kernel methods for surrogate modeling
- 10 Kriging: methods and applications
- Index