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Model Order Reduction and Applications: Cetraro, Italy 2021 1st ed. 2023 [Mīkstie vāki]

  • Formāts: Paperback / softback, 230 pages, height x width: 235x155 mm, weight: 464 g, 47 Illustrations, color; 10 Illustrations, black and white; XIV, 230 p. 57 illus., 47 illus. in color., 1 Paperback / softback
  • Sērija : C.I.M.E. Foundation Subseries 2328
  • Izdošanas datums: 21-Jun-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031295625
  • ISBN-13: 9783031295621
  • Mīkstie vāki
  • Cena: 55,83 €*
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  • Formāts: Paperback / softback, 230 pages, height x width: 235x155 mm, weight: 464 g, 47 Illustrations, color; 10 Illustrations, black and white; XIV, 230 p. 57 illus., 47 illus. in color., 1 Paperback / softback
  • Sērija : C.I.M.E. Foundation Subseries 2328
  • Izdošanas datums: 21-Jun-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031295625
  • ISBN-13: 9783031295621
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.
- 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.