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• Introduction
• Number Systems
• Algebraic Equations
• Scalar Calculus
• Vector Calculus.

This state of the art book takes an applications based approach to teaching mathematics to engineering and applied sciences students. The book lays emphasis on associating mathematical concepts with their physical counterparts, training students of engineering in mathematics to help them learn how things work. The book covers the concepts of number systems, algebra equations and calculus through discussions on mathematics and physics, discussing their intertwined history in a chronological order. The book includes examples, homework problems, and exercises. This book can be used to teach a first course in engineering mathematics or as a refresher on basic mathematical physics. Besides serving as core textbook, this book will also appeal to undergraduate students with cross-disciplinary interests as a supplementary text or reader.

• 1. Kinematics and vectors
• 2. Newton's laws, energy, and momentum
• 3. Rotational motion
• 4. Rotation matrices
• 5. Material properties - elasticity
• 6. Harmonic oscillators
• 7. Waves
• 8. The quantum puzzle
• 9. Quantum mechanics
• 10. Quantum electrons
• 11. Quantum electrons in solids
• 12. Thermal physics
• 13. Quantum statistics
• 14. Electromagnetic phenomena
• 15. Fluids.
• (source: Nielsen Book Data)

Providing an overview of the foundations of engineering from a fundamental scientific and physical perspective, this book reinforces the basic scientific and mathematical principles which underpin a range of engineering disciplines and applications. It covers the basics of physics, including quantum physics, as well as some key topics in chemistry, making it a valuable resource for both students and professionals looking to gain a more coherent and interdisciplinary understanding of engineering systems. Throughout, the focus is on common features of physical systems (such as mechanical and electronic resonance), showing how the same underlying principles apply to different disciplines. Problems are provided at the end of each chapter including conceptual questions and examples to demonstrate the practical application of fundamental scientific principles. These include real-world examples, which are solvable using computational packages such as MATLAB.
(source: Nielsen Book Data)

• Cover page; Half title; LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY; Title Page; Copyright; Dedication; CONTENTS; Preface;
• Chapter 1: Introduction;
• Chapter 2: Finite Element Method-A Summary; Overview; FEM Formulation; Matrix Approach; Example 2.1: Analysis of a 2D Truss; General Procedure for Global Matrix Assembly; Example 2.2: Global Matrix for Triangular Elements; Weighted Residual Approach; Galerkin Method; Shape Functions; Convergence and Stability; Example2.3: Heat Transfer in a Slender Steel Bar; Exercise Problems; References;
• Chapter 3: COMSOL-A Modeling Tool for Engineers.

• OverviewCOMSOL Interface; COMSOL Modules; COMSOL Model Library and Tutorials; General Guidelines for Building a Model;
• Chapter 4: COMSOL Models for Physical Systems; Overview; Section 4.1: Static and Dynamic Analysis of Structures; Example 4.1: Stress Analysis for a Thin Plate Under Stationary Loads; Example 4.2: Dynamic Analysis for a Thin Plate: Eigenvalues and Modal Shapes; Example 4.3: Parametric Study for a Bracket Assembly: 3D Stress Analysis; Example 4.4: Buckling of a Column with Triangular Cross-section: Linearized Buckling Analysis.

• Example 4
• .5: Static and Dynamic Analysis for a 2D Bridge-support TrussExample 4
• .6: Static and Dynamic Analysis for a 3D Truss Tower; Section 4
• .2: Dynamic Analysis and Models of Internal Flows: Laminar and Turbulent; Example 4
• .7: Axisymmetric Flow in a Nozzle: Simplified Water-jet; Example 4
• .8: Swirl Flow Around a Rotating Disk: Laminar Flow; Example 4
• .9: Swirl Flow Around a Rotating Disk: Turbulent Flow; Example 4
• .10: Flow in a U-shape Pipe with Square Cross-sectional Area: Laminar Flow; Example 4
• .11: Double-driven Cavity Flow: Moving Boundary Conditions.

• Example 4
• .12: Water Hammer Model: Transient Flow AnalysisExample 4
• .13: Static Fluid Mixer Model; Section 4
• .3: Modeling of Steady and Unsteady Heat Transfer in Media; Example 4
• .14: Heat Transfer in a Multilayer Sphere; Example 4
• .15: Heat Transfer in a Hexagonal Fin; Example 4
• .16: Transient Heat Transfer Through a Nonprismatic Fin with Convective Cooling; Example 4
• .17: Heat Conduction Through a Multilayer Wall with Contact Resistance; Section 4
• .4: Modeling of Electrical Circuits; Example 4
• .18: Modeling an RC Electrical Circuit; Example 4
• .19: Modeling an RLC Electrical Circuit.

• Section 4
• .5: Modeling Complex and Multiphysics ProblemsExample 4
• .20: Stress Analysis for an Orthotropic Thin Plate; Example 4
• .21: Thermal Stress Analysis and Transient Response of a Bracket; Example 4
• .22: Static Fluid Mixer with Flexible Baffles; Example 4
• .23: Double Pendulum: Multibody Dynamics; Example 4
• .24: Multiphysics Model for Thermoelectric Modules; Example 4
• .25: Acoustic Pressure Wave Propagation in an Automotive Muffler; Exercise Problems; References; Suggested Further Readings; Trademark References; Index.

• 1: Introduction.
• 2: Finite Element Method (FEM)-A Summary.
• 3: COMSOL - A Modeling Tool For Engineers.
• 4: Modeling Single-Physics Problems.
• 5: Modeling Multi-Physics Problems.
• 6: Modeling Energy Systems With COMSOL.
• 7: Advanced Features Of COMSOL.
• Appendices.
• Index.
• (source: Nielsen Book Data)

COMSOL Multiphysics software is one of the most valuable software modeling tools for engineers and scientists. This book is designed for engineers from the fields of mechanical, electrical, and civil disciplines, and introduces multiphysics modeling techniques and examples accompanied by practical applications using COMSOL 4.x. The book includes a companion CD-ROM with files of over 25 models, images, and code. The main objective is to introduce readers to use COMSOL as an engineering tool for modeling by solving examples that could become a guide for modeling similar or more complicated problems. The objective is to provide a collection of examples and modeling guidelines through which readers can build their own models. Readers are assumed to be familiar with the principles of numerical modeling and the finite element method (FEM). The book takes a flexible-level approach for presenting the materials along with using practical examples. The mathematical fundamentals, engineering principles, and design criteria are presented as integral parts of examples. At the end of each chapter are references that contain more in-depth physics, technical information, and data; these are referred to throughout the book and used in the examples. "COMSOL for Engineers" could be used to complement another text that provides background training in engineering computations and methods. Examples provided in this book should be considered as "lessons" for which background physics could be explained in more detail. FEATURES Includes a companion CD-ROM with files of over 25 models, images, code Uses progressive approach in terms of examples and models.
(source: Nielsen Book Data)

• Preface
• 1. Introduction to Computer Simulation 1.1 Historical Background 1.2 Theory, Modeling and Simulation in Physics 1.3 Reductionism in Physics 1.4 Basics of Ordinary and Partial Differential Equations in Physics 1.5 Numerical Solution of Differential Equations: Mesh-Based vs. Particle Methods 1.6 The Role of Algorithms in Scientific Computing 1.7 Remarks on Software Design 1.8 Summary
• 2. Fundamentals of Statistical Physics 2.1 Introduction 2.2 Elementary Statistics 2.3 Introduction to Classical Statistical Mechanics 2.4 Introduction to Thermodynamics 2.5 Summary
• 3. Inter- and Intramolecular Short-Range Potentials 3.1 Introduction 3.2 Quantum Mechanical Basis of Intermolecular Interactions 3.2.1 Perturbation Theory 3.3 Classical Theories of Intermolecular Interactions 3.4 Potential Functions 3.5 Molecular Systems 3.6 Summary
• 4. Molecular Dynamics Simulation 4.1 Introduction 4.2 Basic Ideas of MD 4.3 Algorithms for Calculating Trajectories 4.4 Link between MD and Quantum Mechanics 4.5 Basic MD Algorithm: Implementation Details 4.6 Boundary Conditions 4.7 The Cutoff Radius for Short-Range Potentials 4.8 Neighbor Lists: The Linked-Cell Algorithm 4.9 The Method of Ghost Particles 4.10 Implementation Details of the Ghost Particle Method 4.11 Making Measurements 4.12 Ensembles and Thermostats 4.13 Case Study: Impact of Two Different Bodies 4.14 Case Study: Rayleigh-Taylor Instability 4.15 Case Study: Liquid-Solid Phase Transition of Argon
• 5. Advanced MD Simulation 5.1 Introduction 5.2 Parallelization 5.3 More Complex Potentials and Molecules 5.4 Many Body Potentials 5.5 Coarse Grained MD for Mesoscopic Systems
• 6. Outlook on Monte Carlo Simulations 6.1 Introduction 6.2 The Metropolis Monte-Carlo Method 6.2.1 Calculation of Volumina and Surfaces 6.2.2 Percolation Theory 6.3 Basic MC Algorithm: Implementation Details 6.3.1 Case Study: The 2D Ising Magnet 6.3.2 Trial Moves and Pivot Moves 6.3.3 Case Study: Combined MD and MC for Equilibrating a Gaussian Chain 6.3.4 Case Study: MC of Hard Disks 6.3.5 Case Study: MC of Hard Disk Dumbbells in 2D 6.3.6 Case Study: Equation of State for the Lennard-Jones Fluid 6.4 Rosenbluth and Rosenbluth Method 6.5 Bond Fluctuation Model 6.6 Monte Carlo Simulations in Different Ensembles 6.7 Random Numbers Are Hard to Find
• 7. Applications from Soft Matter and Shock Wave Physics 7.1 Biomembranes 7.2 Scaling Properties of Polymers 7.3 Polymer Melts 7.4 Polymer Networks as a Model for the Cytoskeleton of Cells 7.5 Shock Wave Impact in Brittle Solids
• 8. Concluding Remarks A Appendix A.1 Quantum Statistics of Ideal Gases A.2 Maxwell-Boltzmann, Bose-Einstein- and Fermi-Dirac Statistics A.3 Stirling's Formula A.4 Useful Integrals in Statistical Physics A.3 Useful Conventions for Implementing Simulation Programs A.4 Quicksort and Heapsort Algorithms A.4 Selected Solutions to Exercises Abbreviations Bibliography Index.
• (source: Nielsen Book Data)

This work is a needed reference for widely used techniques and methods of computer simulation in physics and other disciplines, such as materials science. Molecular dynamics computes a molecule's reactions and dynamics based on physical models; Monte Carlo uses random numbers to image a system's behaviour when there are different possible outcomes with related probabilities. The work conveys both the theoretical foundations as well as applications and "tricks of the trade", that often are scattered across various papers. Thus it will meet a need and fill a gap for every scientist who needs computer simulations for his/her task at hand. In addition to being a reference, case studies and exercises for use as course reading are included.
(source: Nielsen Book Data)

• Preface
• 1. Introduction to Computer Simulation 1.1 Historical Background 1.2 Theory, Modeling and Simulation in Physics 1.3 Reductionism in Physics 1.4 Basics of Ordinary and Partial Differential Equations in Physics 1.5 Numerical Solution of Differential Equations: Mesh-Based vs. Particle Methods 1.6 The Role of Algorithms in Scientific Computing 1.7 Remarks on Software Design 1.8 Summary
• 2. Fundamentals of Statistical Physics 2.1 Introduction 2.2 Elementary Statistics 2.3 Introduction to Classical Statistical Mechanics 2.4 Introduction to Thermodynamics 2.5 Summary
• 3. Inter- and Intramolecular Short-Range Potentials 3.1 Introduction 3.2 Quantum Mechanical Basis of Intermolecular Interactions 3.2.1 Perturbation Theory 3.3 Classical Theories of Intermolecular Interactions 3.4 Potential Functions 3.5 Molecular Systems 3.6 Summary
• 4. Molecular Dynamics Simulation 4.1 Introduction 4.2 Basic Ideas of MD 4.3 Algorithms for Calculating Trajectories 4.4 Link between MD and Quantum Mechanics 4.5 Basic MD Algorithm: Implementation Details 4.6 Boundary Conditions 4.7 The Cutoff Radius for Short-Range Potentials 4.8 Neighbor Lists: The Linked-Cell Algorithm 4.9 The Method of Ghost Particles 4.10 Implementation Details of the Ghost Particle Method 4.11 Making Measurements 4.12 Ensembles and Thermostats 4.13 Case Study: Impact of Two Different Bodies 4.14 Case Study: Rayleigh-Taylor Instability 4.15 Case Study: Liquid-Solid Phase Transition of Argon
• 5. Advanced MD Simulation 5.1 Introduction 5.2 Parallelization 5.3 More Complex Potentials and Molecules 5.4 Many Body Potentials 5.5 Coarse Grained MD for Mesoscopic Systems
• 6. Outlook on Monte Carlo Simulations 6.1 Introduction 6.2 The Metropolis Monte-Carlo Method 6.2.1 Calculation of Volumina and Surfaces 6.2.2 Percolation Theory 6.3 Basic MC Algorithm: Implementation Details 6.3.1 Case Study: The 2D Ising Magnet 6.3.2 Trial Moves and Pivot Moves 6.3.3 Case Study: Combined MD and MC for Equilibrating a Gaussian Chain 6.3.4 Case Study: MC of Hard Disks 6.3.5 Case Study: MC of Hard Disk Dumbbells in 2D 6.3.6 Case Study: Equation of State for the Lennard-Jones Fluid 6.4 Rosenbluth and Rosenbluth Method 6.5 Bond Fluctuation Model 6.6 Monte Carlo Simulations in Different Ensembles 6.7 Random Numbers Are Hard to Find
• 7. Applications from Soft Matter and Shock Wave Physics 7.1 Biomembranes 7.2 Scaling Properties of Polymers 7.3 Polymer Melts 7.4 Polymer Networks as a Model for the Cytoskeleton of Cells 7.5 Shock Wave Impact in Brittle Solids
• 8. Concluding Remarks A Appendix A.1 Quantum Statistics of Ideal Gases A.2 Maxwell-Boltzmann, Bose-Einstein- and Fermi-Dirac Statistics A.3 Stirling's Formula A.4 Useful Integrals in Statistical Physics A.3 Useful Conventions for Implementing Simulation Programs A.4 Quicksort and Heapsort Algorithms A.4 Selected Solutions to Exercises Abbreviations Bibliography Index.
• (source: Nielsen Book Data)

This work is a needed reference for widely used techniques and methods of computer simulation in physics and other disciplines, such as materials science. Molecular dynamics computes a molecule's reactions and dynamics based on physical models; Monte Carlo uses random numbers to image a system's behaviour when there are different possible outcomes with related probabilities. The work conveys both the theoretical foundations as well as applications and "tricks of the trade", that often are scattered across various papers. Thus it will meet a need and fill a gap for every scientist who needs computer simulations for his/her task at hand. In addition to being a reference, case studies and exercises for use as course reading are included.
(source: Nielsen Book Data)

• Chaos in Laser Systems
• Semiconductor Lasers and Theory
• Theory of Optical Feedback in Semiconductor Lasers
• Dynamics of Semiconductor Lasers with Optical Feedback
• Dynamics in Semiconductor Lasers with Optical Injection
• Dynamics of Semiconductor Lasers with Optoelectronic Feedback and Modulation
• Instability and Chaos in Various Laser Structures
• Chaos Control and Applications
• Stabilization of Semiconductor Lasers
• Metrology Based on Chaotic Semiconductor Lasers
• Chaos Synchronization in Semiconductor Lasers
• Chaotic Communications in Semiconductor Lasers
• Physical Random Number Generations and Photonic Integrated Circuits for Chaotic Generators.

This third edition of "Semiconductor Lasers, Stability, Instability and Chaos" was significantly extended. In the previous edition, the dynamics and characteristics of chaos in semiconductor lasers after the introduction of the fundamental theory of laser chaos and chaotic dynamics induced by self-optical feedback and optical injection was discussed. Semiconductor lasers with new device structures, such as vertical-cavity surface-emitting lasers and broad-area semiconductor lasers, are interesting devices from the viewpoint of chaotic dynamics since they essentially involve chaotic dynamics even in their free-running oscillations. These topics are also treated with respect to the new developments in the current edition. Also the control of such instabilities and chaos control are critical issues for applications. Another interesting and important issue of semiconductor laser chaos in this third edition is chaos synchronization between two lasers and the application to optical secure communication. One of the new topics in this edition is fast physical number generation using chaotic semiconductor lasers for secure communication and development of chaos chips and their application. As other new important topics, the recent advance of new semiconductor laser structures is presented, such as quantum-dot semiconductor lasers, quantum-cascade semiconductor lasers, vertical-cavity surface-emitting lasers and physical random number generation with application to quantum key distribution. Stabilities, instabilities, and control of quantum-dot semiconductor lasers and quantum-cascade lasers are important topics in this field.
(source: Nielsen Book Data)

• Preface
• Contents
• Contributors
• Chapter 1: Simulation of Quantum Ballistic Transport in FinFETs
• 1.1 Introduction
• 1.2 Quantum Effects in FinFETs
• 1.2.1 Quantum Confinement
• 1.2.2 Quantum-Mechanical Tunneling
• 1.2.3 Ballistic Transport and Quantum Interference
• 1.3 Self-Consistent Field Method
• 1.4 The NEGF in Real-Space Representation
• 1.5 Computationally Efficient Methods in the Real Space
• 1.5.1 The Recursive GreenÂ?s Function Algorithm
• 1.5.2 The Contact Block Reduction Method
• 1.5.3 The Gauss Elimination Method

• 1.5.4 Computational Efficiency Comparison1.6 The NEGF in Mode-Space Representation
• 1.6.1 Coupled Mode-Space Approach
• 1.6.2 Partial-Coupled Mode-Space Approach
• 1.6.3 Validation of the PCMS Approach
• 1.7 Conclusion
• References
• Chapter 2: Model for Quantum Confinement in Nanowires and the Application of This Model to the Study of Carrier Mobility in Na ...
• 2.1 Introduction
• 2.2 Surface Energy
• 2.3 Thermodynamic Imbalance
• 2.4 Nanowire Surface Disorder
• 2.5 Quantum Confinement
• 2.6 Energy Band Gap as Function of Nanowire Diameter

• 2.7 Formula for Amorphicity2.8 Models for Carrier Scattering
• 2.9 Calculated Carrier Mobility
• 2.10 Conclusions
• References
• Chapter 3: Understanding the FinFET Mobility by Systematic Experiments
• 3.1 Introduction
• 3.2 Impact of Surface Orientation
• 3.3 Impact of Strain
• 3.4 Impact of Fin Doping
• 3.5 Impact of Gate Stack
• 3.6 Conclusion
• References
• Chapter 4: Quantum Mechanical Potential Modeling of FinFET
• 4.1 Introduction
• 4.2 FinFET Structure
• 4.2.1 FinFET Design Parameters
• 4.3 Quantum Mechanical Potential Modeling

• 4.4 Threshold Voltage Modeling4.5 Source/Drain Resistance Modeling
• 4.6 Results and Discussion
• 4.7 Conclusion
• References
• Chapter 5: Physical Insight and Correlation Analysis of Finshape Fluctuations and Work-Function Variability in FinFET Devices
• 5.1 Introduction
• 5.2 Modeling Approach
• 5.2.1 LER Modeling
• 5.2.2 WFV Modeling
• 5.3 Statistical Analysis of LER- and WFV-Induced Fluctuations
• 5.4 Correlation-Based Approaches for Variability Estimation
• 5.4.1 Correlations and Sensitivity Analysis

• 5.4.2 Simplified Approaches for Variability Estimation5.4.2.1 Threshold Voltage Variability
• 5.4.2.2 Drive Current Variability
• 5.4.3 Physical Insight of Fin LER-Induced Threshold Voltage Increase
• 5.5 Asymmetric Impact of Localized Fluctuations
• 5.5.1 Impact of Local Fin Thinning
• 5.5.2 Impact of Grain Location and Size
• 5.6 Conclusions
• References
• Chapter 6: Characteristic and Fluctuation of Multi-fin FinFETs
• 6.1 Introduction
• 6.1.1 Random Dopant Fluctuation
• 6.1.2 Reduction Techniques of Random Dopant Fluctuation

This book reviews a range of quantum phenomena in novel nanoscale transistors called FinFETs, including quantized conductance of 1D transport, single electron effect, tunneling transport, etc. The goal is to create a fundamental bridge between quantum FinFET and nanotechnology to stimulate readers' interest in developing new types of semiconductor technology. Although the rapid development of micro-nano fabrication is driving the MOSFET downscaling trend that is evolving from planar channel to nonplanar FinFET, silicon-based CMOS technology is expected to face fundamental limits in thenear future. Therefore, new types of nanoscale devices are being investigated aggressively to take advantage of the quantum effect in carrier transport. The quantum confinement effect of FinFET at room temperatures was reported following the breakthrough to sub-10nm scale technology in silicon nanowires. With chapters written by leading scientists throughout the world, "Toward Quantum FinFET" provides a comprehensive introduction to the field as well as a platform for knowledge sharing and dissemination of the latest advances. As a roadmap to guide further research in an area of increasing importance for the future development of materials science, nanofabrication technology, and nano-electronic devices, the book can be recommended for Physics, Electrical Engineering, and Materials Science departments, and as a reference on micro-nano electronic science and device design.
(source: Nielsen Book Data)

• Preface
• 1. Introduction to Computer Simulation 1.1 Historical Background 1.2 Theory, Modeling and Simulation in Physics 1.3 Reductionism in Physics 1.4 Basics of Ordinary and Partial Differential Equations in Physics 1.5 Numerical Solution of Differential Equations: Mesh-Based vs. Particle Methods 1.6 The Role of Algorithms in Scientific Computing 1.7 Remarks on Software Design 1.8 Summary
• 2. Fundamentals of Statistical Physics 2.1 Introduction 2.2 Elementary Statistics 2.3 Introduction to Classical Statistical Mechanics 2.4 Introduction to Thermodynamics 2.5 Summary
• 3. Inter- and Intramolecular Short-Range Potentials 3.1 Introduction 3.2 Quantum Mechanical Basis of Intermolecular Interactions 3.2.1 Perturbation Theory 3.3 Classical Theories of Intermolecular Interactions 3.4 Potential Functions 3.5 Molecular Systems 3.6 Summary
• 4. Molecular Dynamics Simulation 4.1 Introduction 4.2 Basic Ideas of MD 4.3 Algorithms for Calculating Trajectories 4.4 Link between MD and Quantum Mechanics 4.5 Basic MD Algorithm: Implementation Details 4.6 Boundary Conditions 4.7 The Cutoff Radius for Short-Range Potentials 4.8 Neighbor Lists: The Linked-Cell Algorithm 4.9 The Method of Ghost Particles 4.10 Implementation Details of the Ghost Particle Method 4.11 Making Measurements 4.12 Ensembles and Thermostats 4.13 Case Study: Impact of Two Different Bodies 4.14 Case Study: Rayleigh-Taylor Instability 4.15 Case Study: Liquid-Solid Phase Transition of Argon
• 5. Advanced MD Simulation 5.1 Introduction 5.2 Parallelization 5.3 More Complex Potentials and Molecules 5.4 Many Body Potentials 5.5 Coarse Grained MD for Mesoscopic Systems
• 6. Outlook on Monte Carlo Simulations 6.1 Introduction 6.2 The Metropolis Monte-Carlo Method 6.2.1 Calculation of Volumina and Surfaces 6.2.2 Percolation Theory 6.3 Basic MC Algorithm: Implementation Details 6.3.1 Case Study: The 2D Ising Magnet 6.3.2 Trial Moves and Pivot Moves 6.3.3 Case Study: Combined MD and MC for Equilibrating a Gaussian Chain 6.3.4 Case Study: MC of Hard Disks 6.3.5 Case Study: MC of Hard Disk Dumbbells in 2D 6.3.6 Case Study: Equation of State for the Lennard-Jones Fluid 6.4 Rosenbluth and Rosenbluth Method 6.5 Bond Fluctuation Model 6.6 Monte Carlo Simulations in Different Ensembles 6.7 Random Numbers Are Hard to Find
• 7. Applications from Soft Matter and Shock Wave Physics 7.1 Biomembranes 7.2 Scaling Properties of Polymers 7.3 Polymer Melts 7.4 Polymer Networks as a Model for the Cytoskeleton of Cells 7.5 Shock Wave Impact in Brittle Solids
• 8. Concluding Remarks A Appendix A.1 Quantum Statistics of Ideal Gases A.2 Maxwell-Boltzmann, Bose-Einstein- and Fermi-Dirac Statistics A.3 Stirling's Formula A.4 Useful Integrals in Statistical Physics A.3 Useful Conventions for Implementing Simulation Programs A.4 Quicksort and Heapsort Algorithms A.4 Selected Solutions to Exercises Abbreviations Bibliography Index.
• (source: Nielsen Book Data)

This work is a needed reference for widely used techniques and methods of computer simulation in physics and other disciplines, such as materials science. Molecular dynamics computes a molecule's reactions and dynamics based on physical models; Monte Carlo uses random numbers to image a system's behaviour when there are different possible outcomes with related probabilities. The work conveys both the theoretical foundations as well as applications and "tricks of the trade", that often are scattered across various papers. Thus it will meet a need and fill a gap for every scientist who needs computer simulations for his/her task at hand. In addition to being a reference, case studies and exercises for use as course reading are included.
(source: Nielsen Book Data)