Optimal Control of Nonlinear Parabolic Systems
Theory, Algorithms, and Applications
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Optimal Control of Nonlinear Parabolic Systems by Pekka Neittaanmaki
Book DescriptionThis book discusses theoretical approaches to the study of optimal control problems governed by non-linear evolutions - including semi-linear equations, variational inequalities and systems with phase transitions. It also provides algorithms for solving non-linear parabolic systems and multiphase Stefan-like systems.
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Book DetailsISBN: 9780824790813
(279mm x 216mm x 26mm)
Imprint: Marcel Dekker Inc
Publisher: Taylor & Francis Inc
Publish Date: 8-Feb-1994
Country of Publication: United States
Books By Author Pekka Neittaanmaki
Reliable Methods for Computer Simulation, Hardback (August 2004)
Presents the main approaches developed for a posteriori error estimation in various problems. This book contains a number of mathematical results and lists a posteriori error estimation methods. It includes computable bounds of approximation, errors checking algorithms, iteration processes, finite element methods, and more.
Evolutionary Algorithms in Engineering and Computer Science, Hardback (April 1999)
Genetic algorithms (GA) and evolution strategies (ES) are relatively new stochastic based techniques for solving engineering problems on computers. GA and ES are based on a loose biological analogy: evolutionary theory (mutation, crossover, selection, survival of the fittest).
Finite Element Methods, Paperback (January 1998)
Based on the proceedings of the first conference on superconvergence held at the University of Jyvaskyla, Finland, this title presents reviewed papers focusing on superconvergence phenomena in the finite element method. It surveys superconvergence techniques, including their proofs.
Finite Element Approximation for Optimal Shape, Material and Topology Design, Hardback (April 1996)» View all books by Pekka Neittaanmaki
This book addresses the formulation, approximation and numerical solution of optimal shape design problems: from the continuous model through its discretization and approximation results, to sensitivity analysis and numerical realization.
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