Sulaymon L. Eshkabilov - Practical MATLAB Modeling: Programming Ordinary and Partial Differential Equations with Simulink
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Practical MATLAB Modeling: Programming Ordinary and Partial Differential Equations with Simulink: summary, description and annotation
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Employ the essential and hands-on tools and functions of MATLABs ordinary differential equation (ODE) and partial differential equation (PDE) packages, which are explained and demonstrated via interactive examples and case studies. This book contains dozens of simulations and solved problems via m-files/scripts and Simulink models which help you to learn programming and modeling of more difficult, complex problems that involve the use of ODEs and PDEs.
Youll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving more complex engineering and scientific computing problems that require and use differential equations. Practical MATLAB Modeling explains various practical issues of programming and modelling.
After reading and using this book, youll be proficient at using MATLAB and applying the source code from the books examples as templates for your own projects in data science or engineering.
What You Will Learn
- Model complex problems using MATLAB and Simulink
- Gain the programming and modeling essentials of MATLAB using ODEs and PDEs
- Use numerical methods to solve 1st and 2nd order ODEs
- Solve stiff, higher order, coupled, and implicit ODEs
- Employ numerical methods to solve 1st and 2nd order linear PDEs
- Solve stiff, higher order, coupled, and implicit PDEs
Who This Book Is For
Engineers, programmers, data scientists, and students majoring in engineering, applied/industrial math, data science, and scientific computing. This book continues where Apress Beginning MATLAB and Simulink leaves off.
Sulaymon L. Eshkabilov: author's other books
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Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484257982 . For more detailed information, please visit www.apress.com/source-code .
To the memory of my father.
To my mother.
To my wife, Nigora, after 25 wonderful years together.
This book covers the most essential and hands-on tools and functions of the MATLAB and Simulink packages and the Symbolic Math Toolbox (MuPAD notes) to solve, model, and simulate ordinary differential equations (ODEs) and partial differential equations (PDEs). It explains how to solve ODEs and PDEs symbolically and numerically via interactive examples and case studies. The main principle of the book is learn by doing, moving from the simple to the complex. This book contains dozens of solved problems and simulation models embedded in MATLAB scripts and Simulink models, which will help you to master programming and modeling essentials, as well as learn how to program and model more difficult and complex problems that involve ODEs and PDEs.
Practical MATLAB Modeling with Simulinkexplains various practical issues of programming and modeling in parallel by comparing the programming tools of MATLAB to the modeling tools of Simulink. By studying this book, youll be proficient at using the MATLAB/Simulink packages and at using the source codes and models from the books examples as templates for your own projects to solve modeling and simulation, or engineering problems with ODEs and PDEs.
Model complex problems using MATLAB and Simulink
Use MATLAB and Simulink to solve ODEs and PDEs
Use numerical methods to solve first-, second-, and higher-order and coupled ODEs
Solve stiff and implicit ODEs
Employ numerical methods to solve first- and second-order linear PDEs
Solve ODEs symbolically with the Symbolic Math Toolbox of MATLAB
Understand the applications and modeling aspects of differential equations in solving various simulation problems
This book is aimed at engineers, programmers, data scientists, and students majoring in engineering, applied/industrial math, data science, and scientific computing. This book continues where ApressBeginning MATLAB and Simulinkleaves off.
Chapteris dedicated to general formulations and solving ODEs symbolically using The Symbolic Math Toolbox functions and MuPAD note commands.
Chaptercovers the most essential programming aspects to solve first-order ODEs numerically by writing scripts based on the Euler, Runge-Kutta, Milne, Adams-Bashforth, Ralston, and Adams-Moulton methods. Youll write the scripts using MATLABs built-in ODE solvers, such as ode23, ode23t, ode45, ode15s, ode113, odeset, etc., and Simulink modeling.
Chapteris dedicated to solving second-order ODEs symbolically and numerically using the Euler, Runge-Kutta, Milne, Adams-Bashforth, Ralston, and Adams-Moulton methods. Youll do this using MATLABs built-in ODE solvers, such as ode23, ode23t, ode45, ode15s, ode113, odeset, etc., and Simulink modeling.
Chapteraddresses the issues of how to solve stiff ODEs by adjusting a step size, specifying a solver setting, selecting an appropriate solver type, and so forth.
Chapteris dedicated to solving higher-order and coupled ODEs.
Chapteris devoted to solving implicitly defined IVPs and differential algebraic equations using MATLABs ode15i and Simulink modeling.
Chapteris dedicated to a comparative analysis of solving ODEs with MATLABs ODE solvers, Simulink modeling, script writing, and computing analytical solutions with the Symbolic MATH Toolbox (MuPAD notes).
Chaptercovers most essential tools of solving boundary value problems of ODEs numerically by using MATLAB solvers such as bvp4c, bvp5c, bvpinit, deval, etc.
Chapteris devoted to applications of ODEs, specifically, modeling and simulations of spring-mass-damper systems.
Chapteris devoted to applications of ODEs, specifically, modeling and simulations of mechanical and electromechanical systems.
Chapteris devoted to applications of ODEs, specifically, modeling and simulations of trajectory problems.
Chapteris devoted to applications of ODEs, specifically, modeling and simulations of several simulation problems, such as the Lotka-Voltera and Lorenz systems.
Chapteris devoted to solving partial differential equations using PDE toolbox functions, such as pdepe() and the Laplacian operator del2() , and writing scripts based on Gauss-Seidel and finite difference methods.
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