Stable computations by discrete mollification

Stable computations by discrete mollification

Autor / Author: Carlos Daniel Acosta, Carlos Enrique Mejía
Editorial / Publisher: Universidad Nacional de Colombia
Entrega / Delivery : Nacional / International
Envio desde / Ships from: Colombia
Condición / Condition: Nuevo / New

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Tipo: Libro impreso / Print book

Encuadernación / Binding: Tapa blanda / Paperback

Tamaño / Size: 17 x 24 cm

Páginas / Pages: 112

Resumen / Summary:

Autor / Author: Carlos Daniel Acosta, Carlos Enrique Mejía
Editorial / Publisher: Universidad Nacional de Colombia
Entrega / Delivery : Nacional / International
Envio desde / Ships from: Colombia
Condición / Condition: Nuevo / New



Tabla de contenido / Table of contents:

List of Tables 
List of Figures 
Nomenclature 
Acknowledgements 
Foreword 
Abstract 

Chapter 1 
Overview 

1.1 Discrete Mollification 
1.2 Mollification Weights 
1.3 Consistency, Stability and Convergence 
1.4 Thinking About Nonlinearity 
1.5 Boundary Conditions 
1.5.1 Zero Padding Boundary Condition 
1.5.2 Scaled Boundary Condition 
1.5.3 Periodic Boundary Condition 
1.5.4 Boundary Condition by Reflection 
1.6 Parameter Selection 
1.7 Nonlinear Operator 
1.8 Looking Back 
1.9 Concluding Remarks 

Chapter 2 
Convection dominated diffusive problems 

2.1 The Basic Scheme 
2.2 Two Mollified Explicit Schemes 
2.2.1 Stability 
2.2.2 Consistency 
2.3 Numerical Experiments 
2.4 Concluding Remarks 

Chapter 3 
Conservation laws 

3.1 Mollified Lax-Friedrichs Schemes (MLxF) 
3.1.1 Consistency of Mollified Schemes 
3.1.2 Linear Stability Analysis 
3.2 Mollified Nessyahu-Tadmor (MNT) Schemes 
3.2.1 Methods MNT1 and MNT2 
3.2.2 Method MNT3 
3.3 Numerical Experiments 
3.4 Concluding Remarks 

Chapter 4 
Nonllnear and degenerate diffusive problems 

4.1 Mollified Operator Splitting 
4.1.1 The Diffusion Step 
4.1.2 The Operator Splitting Method 
4.1.3 The Nonlinear Convection-Diffusion Equation 
4.1.4 Implementation 
4.1.5 Numerical Experiments 
4.1.6 Concluding Remarks 
4.2 Strongly Degenerate Parabolic Equations 
4.2.1 The Schemes 
4.2.2 Example: Sedimentation
4.3 Concluding Remarks 

Chapter 5 
System identification 

5.1 A Diffusion Coefficient 
5.1.1 Direct Problem 
5.1.2 Inverse Problem 
5.2 Numerical Algorithms 
5.2.1 Coefficient K(x,u) = 1 +a(x)u2 
5.2.2 CoefficientK(x,u)=a(x)+u2 
5.3 Numerical Experiments 
5.4 Strongly Degenerate Parabolic Equations 
5.5 Para meter Identification 
5.6 Discretization of the Inverse Problem 
5.7 Numerical Experiments 
5.8 Concluding Remarks 

Chapter 6 
Literature review 

6.1 System Identification 
6.1.1 Source Terms in a 1-D IHCP 
6.1.2 Source Terms in a 2-D IHCP 
6.1.3 Parameters in a Transport Equation 
6.1.4 Parameters in a Drying System 
6.2 Fractional Calculus 
6.2.1 Caputo Fractional Derivatives 
6.2.2 Source Term Identification in a TFDE 
6.3 Multiscale Analysis 
6.4 Concluding Remarks 

Bibliography 
Index 

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