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Herausgeber: 
  • Alfio Quarteroni
  • Gianluigi Rozza
  • Reduced Order Methods for Modeling and Computational Reduction 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2016  
    Genre:  Schulbücher 
     
    Applied mathematics / B / Computational Mathematics and Numerical Analysis / Computer mathematics / Engineering mathematics / Mathematical and Computational Engineering / Mathematical and Computational Engineering Applications / Mathematical Modeling and Industrial Mathematics
    ISBN:  9783319377353 
    EAN-Code: 
    9783319377353 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #09 - MS&A  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  718 gr 
    Seiten:  334 
    Illustration:  X, 334 p. 79 illus., schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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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