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Reduced order methods for modeling and computational reduction

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 mec...

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Detalles Bibliográficos
Autores principales: Quarteroni, Alfio, Rozza, Gianluigi
Lenguaje:eng
Publicado: Springer 2014
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-02090-7
http://cds.cern.ch/record/1742618
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author Quarteroni, Alfio
Rozza, Gianluigi
author_facet Quarteroni, Alfio
Rozza, Gianluigi
author_sort Quarteroni, Alfio
collection CERN
description 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.
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spelling cern-17426182021-04-21T20:56:30Zdoi:10.1007/978-3-319-02090-7http://cds.cern.ch/record/1742618engQuarteroni, AlfioRozza, GianluigiReduced order methods for modeling and computational reductionMathematical Physics and MathematicsThis 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.Springeroai:cds.cern.ch:17426182014
spellingShingle Mathematical Physics and Mathematics
Quarteroni, Alfio
Rozza, Gianluigi
Reduced order methods for modeling and computational reduction
title Reduced order methods for modeling and computational reduction
title_full Reduced order methods for modeling and computational reduction
title_fullStr Reduced order methods for modeling and computational reduction
title_full_unstemmed Reduced order methods for modeling and computational reduction
title_short Reduced order methods for modeling and computational reduction
title_sort reduced order methods for modeling and computational reduction
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-02090-7
http://cds.cern.ch/record/1742618
work_keys_str_mv AT quarteronialfio reducedordermethodsformodelingandcomputationalreduction
AT rozzagianluigi reducedordermethodsformodelingandcomputationalreduction