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Numerical Methods for Evolutionary Differential Equations [Pehme köide]

  • Formaat: Paperback / softback, 408 pages, kõrgus x laius x paksus: 229x152x18 mm, kaal: 732 g, Illustrations
  • Sari: Computational Science & Engineering
  • Ilmumisaeg: 30-Jun-2008
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 0898716527
  • ISBN-13: 9780898716528
Teised raamatud teemal:
  • Formaat: Paperback / softback, 408 pages, kõrgus x laius x paksus: 229x152x18 mm, kaal: 732 g, Illustrations
  • Sari: Computational Science & Engineering
  • Ilmumisaeg: 30-Jun-2008
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 0898716527
  • ISBN-13: 9780898716528
Teised raamatud teemal:
Methods for the numerical simulation of dynamic mathematical models have been the focus of intensive research for well over 60 years, and the demand for better and more efficient methods has grown as the range of applications has increased. Mathematical models involving evolutionary partial differential equations (PDEs) as well as ordinary differential equations (ODEs) arise in many diverse applications such as fluid flow, image processing and computer vision, physics based animation, mechanical systems, relativity, earth sciences, and mathematical finance.

This textbook develops, analyzes, and applies numerical methods for evolutionary, or time-dependent, differential problems. Both partial and ordinary differential equations are discussed from a unified viewpoint. The author emphasizes finite difference and finite volume methods, specifically their principled derivation, stability, accuracy, efficient implementation, and practical performance in various fields of science and engineering. Smooth and nonsmooth solutions for hyperbolic PDEs, parabolic type PDEs, and initial value ODEs are treated, and a practical introduction to geometric integration methods is included as well.

The author bridges theory and practice by developing algorithms, concepts, and analysis from basic principles while discussing efficiency and performance issues and demonstrating methods through examples and case studies from a variety of application areas.

Muu info

Develops, analyses, and applies numerical methods for evolutionary, or time-dependent, differential problems.
Preface xi
Introduction
1(36)
Well-Posed Initial Value Problems
4(8)
Simple model cases
7(3)
More general cases
10(1)
Initial-boundary value problems
11(1)
The solution operator
12(1)
A Taste of Finite Differences
12(12)
Stability ideas
17(7)
Reviews
24(8)
Taylor's theorem
24(1)
Matrix norms and eigenvalues
25(3)
Function spaces
28(1)
The continuous Fourier transform
29(1)
The matrix power and exponential
30(1)
Fourier transform for periodic functions
31(1)
Exercises
32(5)
Methods and Concepts for ODEs
37(54)
Linear Multistep Methods
39(3)
Runge-Kutta Methods
42(6)
Convergence and 0-stability
48(4)
Error Control and Estimation
52(1)
Stability of ODE Methods
53(2)
Stiffness
55(4)
Solving Equations for Implicit Methods
59(5)
Differential-Algebraic Equations
64(2)
Symmetric and One-Sided Methods
66(1)
Highy Oscillatory Problems
66(5)
Boundary Value ODEs
71(1)
Reviews
72(11)
Gaussian elimination and matrix decompositions
73(1)
Polynomial interpolation and divided differences
74(3)
Orthogonal and trigonoetric polynomials
77(2)
Basic quadrature rules
79(1)
Fixed point iteration and Newton's method
80(2)
Discrete and fast Fourier transforms
82(1)
Exereises
83(8)
Finte Difference and Finite volume Methods
91(44)
Semi-Discretization
92(28)
Accuracy and derivation of spatial discretizations
94(4)
Staggered meshes
98(8)
Boundary conditions
106(4)
The finite element method
110(3)
Nonuniform meshes
113(7)
Stability and convergence
120(1)
Full Discretization
120(10)
Order, stability, and convergence
122(6)
General linear stability
128(2)
Exercises
130(5)
Stability for constant Coefficient Problesm
135(16)
Fourier Analysis
135(9)
Stability for scalar equations
137(2)
Stability for systems of equations
139(3)
Semi-discretization stability
142(1)
Fourier analysis and ODE absolute stability regions
143(1)
Eigenvalue Analysis
144(2)
Exercises
146(5)
Variable coefficient and Nolinear Problems
151(30)
Freezing Coefficients and Dissipativity
153(1)
Schemes fo Hyperbolic Systems in One Dimension
154(14)
Lax-Wendroff and variants for conservations laws
156(2)
Leapfrog and Lax-Friedreichs
158(4)
Upwind scheme and Energy Methods
162(3)
Box and Crank-Nicolson
165(3)
Nonlinear Stability and Energy Methods
168(9)
Energy method
169(4)
Runge-Kutta for skew-symmetric semi-discretizations
173(4)
Exercises
177(4)
Hamiltonian systms and Long time Intergration
181(30)
Hamiltonian Systems
182(3)
Symplecctic and Other Relevant Methods
185(10)
Symplectic Runge-Kutta methods
188(1)
Splitting and composition methods
189(5)
Variational methods
194(1)
Properties of Symplectic Methods
195(3)
Pitfalls in Highly Oscillatory Hamiltonian Systems
198(7)
Exercise
205(6)
Dispersion and Dissipation
211(42)
Dispersion
212(5)
The Wave Equation
217(13)
The KdV Equation
230(12)
Schemes based on a classicl semi-discretization
232(4)
Box schemes
236(6)
Spectral methods
242(4)
Lagrangian methods
246(1)
Exercises
246(7)
More on Handling Boundary Conditions
253(22)
parabolic Problems
253(4)
Hyperbolic Problems
257(14)
Boundary conditions for hyperbolic problems
257(4)
Boundary conditions for discretized hyperbolic problems
261(7)
Order reduction for Runge-Kutta Methods
268(3)
Infinite or Large Domains
271(1)
Exercises
272(3)
Several Space Variables and Splitting Methods
275(52)
Extending the Methods We Already Know
276(3)
Solving for Implicit Methods
279(13)
Implicite methods for parabolic equations
282(8)
Alternating direction implicit methods
290(2)
Nonlinear problems
292(1)
Splitting Methods
292(20)
More general splitting
297(9)
Additive methods
306(4)
Exponential time differencing
310(2)
Review: Iterative Methods for Linear Systems
312(8)
Simplest iterative methods
312(2)
Conjugate gradient and related methods
314(3)
Multigrid methods
317(3)
Exercises
320(7)
Discontinuities and Almost Discontinuities
327(38)
Scalar conservation Laws in One Dimension
329(5)
Exact solution of the Riemann problem
333(1)
First Order Schemes for Scalar Conservation Laws
334(6)
Godunov's sheme
338(2)
Higher Order Schemes for Scalar Conservation Laws
340(12)
High-resolution schemes
341(1)
Semi-discretization and ENO schemes
342(4)
Strong Stability preserving methods
346(3)
WENO schemes
349(3)
Systems of Conservation Laws
352(4)
Multidimensional Problems
356(2)
Problems with sharp Layers
358(2)
Exercise
360(5)
Additional Topics
365(10)
What firs: Optimize or Discretize?
365(3)
Symmetric matrices for nonuniform spatila meshes
366(1)
Efficient multigrid and Neumann BCs
366(1)
Optimal control
367(1)
Nonuniform Meshes
368(4)
Adaptive meshes for steady state problems
369(2)
Adaptive mesh refinement
371(1)
Moving meshes
371(1)
Level Set Methods
372(3)
Bibliography 375(12)
Index 387
Uri M. Ascher is a Professor of Computer Science at the University of British Columbia, Vancouver.