© 1991

Continuous System Modeling


Table of contents

  1. Front Matter
    Pages i-xxviii
  2. François E. Cellier
    Pages 1-22
  3. François E. Cellier
    Pages 23-50
  4. François E. Cellier
    Pages 51-78
  5. François E. Cellier
    Pages 79-132
  6. François E. Cellier
    Pages 133-200
  7. François E. Cellier
    Pages 201-249
  8. François E. Cellier
    Pages 251-296
  9. François E. Cellier
    Pages 297-346
  10. François E. Cellier
    Pages 347-416
  11. François E. Cellier
    Pages 417-454
  12. François E. Cellier
    Pages 455-506
  13. François E. Cellier
    Pages 507-553
  14. François E. Cellier
    Pages 555-622
  15. François E. Cellier
    Pages 623-702
  16. François E. Cellier
    Pages 703-741
  17. Back Matter
    Pages 743-755

About this book


Modeling and Simulation have become endeavors central to all disciplines of science and engineering. They are used in the analysis of physical systems where they help us gain a better understanding of the functioning of our physical world. They are also important to the design of new engineering systems where they enable us to predict the behavior of a system before it is ever actually built. Modeling and simulation are the only techniques available that allow us to analyze arbitrarily non-linear systems accurately and under varying experimental conditions. Continuous System Modeling introduces the student to an important subclass of these techniques. They deal with the analysis of systems described through a set of ordinary or partial differential equations or through a set of difference equations. This volume introduces concepts of modeling physical systems through a set of differential and/or difference equations. The purpose is twofold: it enhances the scientific understanding of our physical world by codifying (organizing) knowledge about this world, and it supports engineering design by allowing us to assess the consequences of a particular design alternative before it is actually built. This text has a flavor of the mathematical discipline of dynamical systems, and is strongly oriented towards Newtonian physical science.


3D algorithm algorithms artificial neural network design genetic algorithms kinetics linear optimization mechanics model modeling simulation system modeling thermodynamics

Authors and affiliations

  1. 1.Department of Electrical and Computer Engineering and Applied Mathematics ProgramUniversity of ArizonaTucsonUSA

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