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Download the fantastic book titled Reduced Order Methods for Modeling and Computational Reduction written by Alfio Quarteroni, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Reduced Order Methods for Modeling and Computational Reduction", which was released on 05 June 2014. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Mathematics genre.

Summary of Reduced Order Methods for Modeling and Computational Reduction by Alfio Quarteroni PDF

This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.

Reduced Order Methods for Modeling and Computational Reduction

Detail About Reduced Order Methods for Modeling and Computational Reduction PDF

  • Author : Alfio Quarteroni
  • Publisher : Springer
  • Genre : Mathematics
  • Total Pages : 338 pages
  • ISBN : 3319020900
  • PDF File Size : 42,5 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Reduced Order Methods for Modeling and Computational Reduction

Reduced Order Methods for Modeling and Computational Reduction
  • Publisher : Springer
  • File Size : 41,5 Mb
  • Release Date : 05 June 2014
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This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with

Model Order Reduction: Theory, Research Aspects and Applications

Model Order Reduction: Theory, Research Aspects and Applications
  • Publisher : Springer Science & Business Media
  • File Size : 52,9 Mb
  • Release Date : 27 August 2008
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The idea for this book originated during the workshop “Model order reduction, coupled problems and optimization” held at the Lorentz Center in Leiden from S- tember 19–23, 2005. During one of the

Snapshot-Based Methods and Algorithms

Snapshot-Based Methods and Algorithms
  • Publisher : Walter de Gruyter GmbH & Co KG
  • File Size : 51,5 Mb
  • Release Date : 16 December 2020
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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

Interpolatory Methods for Model Reduction

Interpolatory Methods for Model Reduction
  • Publisher : SIAM
  • File Size : 35,6 Mb
  • Release Date : 13 January 2020
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Dynamical systems are a principal tool in the modeling, prediction, and control of a wide range of complex phenomena. As the need for improved accuracy leads to larger and more

Reduced Basis Methods for Partial Differential Equations

Reduced Basis Methods for Partial Differential Equations
  • Publisher : Springer
  • File Size : 46,5 Mb
  • Release Date : 19 August 2015
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This book provides a basic introduction to reduced basis (RB) methods for problems involving the repeated solution of partial differential equations (PDEs) arising from engineering and applied sciences, such as

Data-Driven Science and Engineering

Data-Driven Science and Engineering
  • Publisher : Cambridge University Press
  • File Size : 41,6 Mb
  • Release Date : 05 May 2022
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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Model Reduction of Parametrized Systems

Model Reduction of Parametrized Systems
  • Publisher : Springer
  • File Size : 28,7 Mb
  • Release Date : 05 September 2017
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The special volume offers a global guide to new concepts and approaches concerning the following topics: reduced basis methods, proper orthogonal decomposition, proper generalized decomposition, approximation theory related to model