Showing posts with label physics. Show all posts
Showing posts with label physics. Show all posts

Computational Electrodynamics: The Finite-Difference Time-Domain Method, Third Edition Review

Computational Electrodynamics: The Finite-Difference Time-Domain Method, Third Edition
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I cannot quite honestly give this book (*first* edition, not second) a full five-point-zero stars because it somewhat comes apart the closer one gets to the final chapters. I read this book a few years ago, so I apologize for lack of specificity. However, I completely agree with the prior reviewer who stated that this book is better than Kunz's and Luebbers' book, which I appears to be a slightly edited compilation of previous publications --- even if that is completely untrue. In fact, in my opinion, Taflove's book (again, first edition) is a *much* better textbook than Kunz and Luebbers.
The Book News review is somewhat misleading. Taflove derives the difference equations in full, painstaking detail. (Perhaps the Book News reviewer fell asleep during that portion.) For me, this was the most valuable and educational portion of the book. Example applications have their place, but only after understanding the basic principles. Taflove did an excellent job in describing these principles, which go far beyond the basic Yee algorithm (e.g. extrapolation techniques and incorporation of BC's). Those readers familiar with other FD books should understand what I'm saying here: Anyone who reads this book and understands it will not only be conversant about FDTD but should also be able to write solid working codes. With the K&L book, this is very questionable.

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This extensively revised and expanded third edition of the Artech House bestseller, Computational Electrodynamics: The Finite-Difference Time-Domain Method, offers engineers the most up-to-date and definitive resource on this critical method for solving Maxwell's equations. The method helps practitioners design antennas, wireless communications devices, high-speed digital and microwave circuits, and integrated optical devices with unsurpassed efficiency. There has been considerable advancement in FDTD computational technology over the past few years, and the third edition brings professionals the very latest details with entirely new chapters on important techniques, major updates on key topics, and new discussions on emerging areas such as nanophotonics. What's more, to supplement the third edition, the authors have created a Web site with solutions to problems, downloadable graphics and videos, and updates, making this new edition the ideal textbook on the subject as well.

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Dynamical Processes on Complex Networks Review

Dynamical Processes on Complex Networks
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A clearly written, authoritative account of the mechanisms acting on complex networks, this book is indispensable to social scientists, physicists, web researchers, and biologists alike. The text is well-organized and provides a structured, rigorous introduction to network science in the first three chapters and continues into the theory of dynamical processes, phase transitions, robustness, synchronization phenomena, random walks, epidemic spreading and diffusion processes, opinion formation in social networks, traffic modeling and systems biology. Chapters begin with a strong case for the importance of each particular topic, and move quickly into lucid mathematical accounts of the respective processes and their statistical properties. Chapters frequently conclude with a philosophical bookend that outlines theoretical implications and future directions for the field. Another strength of this work is that it is structured in such a way that chapters can be utilized individually, each one acting a complete, comprehensive unit of knowledge.
This book is a definitive guide to understanding a wide range of dynamical processes on networks, and it's rigor and scope afford the reader a high degree of confidence in the material. The only caveat I might offer is that the text is technically dense, thoroughly covering a broad swath of science in just over 300 pages. The reader is presented with everything required to understand the concepts covered in each chapter, but I often found myself re-reading passages in order to fully understand the arguments and implications of the text. This aside, rest assured that the studious reader will find this to be a rewarding, thoughtful account of an important field of science, and I would strongly recommend this book to anyone whose work involves network analysis.

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Understanding Molecular Simulation, Second Edition: From Algorithms to Applications (Computational Science Series, Vol 1) Review

Understanding Molecular Simulation, Second Edition: From Algorithms to Applications (Computational Science Series, Vol 1)
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This book covers many interesting topics in molecular simulation, both Monte Carlo and M.D. It focuses on understanding the main ideas rather than giving long codes. It's a good place to start, but it also covers some ideas not found in many other books. When I try to extend my molecular dynamics program I always check what Frenkel and Smit have to say about it.

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Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program. The implementation of simulation methods is illustrated in pseudocodes and their practical use in the case studies used in the text.Since the first edition only five years ago, the simulation world has changed significantly -- current techniques have matured and new ones have appeared.This new edition deals with these new developments; in particular, there are sections on: Transition path sampling and diffusive barrier crossing to simulaterare events Dissipative particle dynamic as a course-grained simulation technique Novel schemes to compute the long-ranged forces Hamiltonian and non-Hamiltonian dynamics in the context constant-temperature and constant-pressure molecular dynamics simulations Multiple-time step algorithms as an alternative for constraints Defects in solids The pruned-enriched Rosenbluth sampling, recoil-growth, and concerted rotations for complex molecules Parallel tempering for glassy HamiltoniansExamples are included that highlight current applications and the codes of case studies are available on the World Wide Web. Several new examples have been added since the first edition to illustrate recent applications. Questions are included in this new edition. No prior knowledge of computer simulation is assumed.

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Dynamic Simulations of Multibody Systems Review

Dynamic Simulations of Multibody Systems
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This book is very unique in that it successfully combines
Physically Based Modeling with Computational Geometry.
Everyone in developing realtime dynamic simulation systems
with 3-dimensional computer graphics should have one.

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This book introduces the techniques needed to produce realistic simulations and animations of particle and rigid body systems. It focuses on both the theoretical and practical aspects of developing and implementing physically based dynamic simulation engines that can be used to generate convincing animations of physical events involving particles and rigid bodies. It can also be used to produce accurate simulations of mechanical systems, such as a robotic parts feeder. The book is intended for researchers in computer graphics, computer animation, computer-aided mechanical design and modeling software developers.

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An Introduction to Stellar Astrophysics Review

An Introduction to Stellar Astrophysics
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This is one of the best book on the market that covers the most important topics of stellar astrophysics (stellar formation, stellar atmospheres, stellar interiors, stellar evolution, etc.) at the advanced undergraduate level. The sections on the Saha equation, the radiative transfer equation, stellar atmospheres and on nucleosynthesis are excellent. The explanations of the physical phenomena are clear and direct which makes this an excellent textbook.

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An Introduction to Stellar Astrophysics aspires to provide the reader with an intermediate knowledge on stars whilst focusing mostly on the explanation of the functioning of stars by using basic physical concepts and observational results.
The book is divided into seven chapters, featuring both core and optional content:
Basic concepts
Stellar Formation
Radiative Transfer in Stars
Stellar Atmospheres
Stellar Interiors
Nucleosynthesis and Stellar Evolution and
Chemically Peculiar Stars and Diffusion.

Student-friendly features include:
Detailed examples to help the reader better grasp the most important concepts
A list of exercises is given at the end of each chapter and answers to a selection of these are presented.
Brief recalls of the most important physical concepts needed to properly understand stars.
A summary for each chapter
Optional and advanced sections are included which may be skipped without interfering with the flow of the core content.

This book is designed to cover the most important aspects of stellar astrophysics inside a one semester (or half-year) course and as such is relevant for advanced undergraduate students following a first course on stellar astrophysics, in physics or astronomy programs. It will also serve as a basic reference for a full-year course as well as for researchers working in related fields.

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How Nature Works: The Science of Self-organized Criticality Review

How Nature Works: The Science of Self-organized Criticality
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I couldn't let the previous reviewer's comments stand without comment. I can't believe the reviewer read the same book that I did. Bak's treatment is detailed, clear, and balanced. When he is enthusiastic he let's you know exactly why, leaving you free to make up your own mind. The fact that most of the studies he describes were published in Physical Review Letters might tell you something about their quality. The book provides wonderful examples of the role of models in science, much better than any I've come across in rather extensive search for materials for a course on the Nature of Science I help teach. I'm reading the book for the third time (not because it is difficult to read, but simply because it repays rereading) and I admire it more with each reading. If you want to understand models that display Self Organized Criticality, this book is without question the place to go.

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A New Kind of Science Review

A New Kind of Science
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This review took almost one year. Unlike many previous referees (rank them by Amazon.com's "most helpful" feature) I read all 1197 pages including notes. Just to make sure I won't miss the odd novel insight hidden among a million trivial platitudes.
On page 27 Wolfram explains "probably the single most surprising discovery I have ever made:" a simple program can produce output that seems irregular and complex.
This has been known for six decades. Every computer science (CS) student knows the dovetailer, a very simple 2 line program that systematically lists and executes all possible programs for a universal computersuch as a Turing machine (TM). It computes all computable patterns, including all those in Wolfram's book, embodies the well-known limits of computability, and is basis of uncountable CS exercises.
Wolfram does know (page 1119) Minsky's very simple universal TMs from the 1960s. Using extensive simulations, he finds a slightly simpler one. New science? Small addition to old science. On page 675 we find a particularly simple cellular automaton (CA) and Matthew Cook's universality proof(?). This might be the most interesting chapter. It reflects that today's PCs are more powerful systematic searchers for simple rules than those of 40 years ago. No new paradigm though.
Was Wolfram at least first to view programs as potential explanations of everything? Nope. That was Zuse. Wolfram mentions him in exactly one line (page 1026): "Konrad Zuse suggested that [the universe] could be a continuous CA." This is totally misleading. Zuse's 1967 paper suggested the universe is DISCRETELY computable, possibly on a DISCRETE CA just like Wolfram's. Wolfram's causal networks (CA's with variable toplogy, chapter 9) will run on any universal CA a la Ulam & von Neumann & Conway & Zuse. Page 715 explains Wolfram's "key unifying idea" of the "principle of computational equivalence:" all processes can be viewed as computations. Well, that's exactly what Zuse wrote 3 decades ago.
Chapter 9 (2nd law of thermodynamics) elaborates (without reference)on Zuse's old insight that entropy cannot really increase in deterministically computed systems, although it often SEEMS to increase. Wolfram extends Zuse's work by a tiny margin, using today's more powerful computers to perform experiments as suggested in Zuse's 1969 book. I find it embarassing how Wolfram tries to suggest it was him who shifted a paradigm, not the legendary Zuse.
Some reviews cite Wolfram's previous reputation as a physicist and software entrepreneur, giving him the benefit of the doubt instead of immediately dismissing him as just another plagiator. Zuse's reputation is in a different league though: He built world's very first general purpose computers (1935-1941), while Wolfram is just one of many creators of useful software (Mathematica). Remarkably, in his history of computing (page 1107) Wolfram appears to try to diminuish Zuse's contributions by only mentioning Aiken's later 1944 machine.
On page 465 ff (and 505 ff on multiway systems) Wolfram asks whether there is a simple program that computes the universe. Here he sounds like Schmidhuber in his 1997 paper "A Computer Scientist's View of Life, the Universe, and Everything." Schmidhuber applied the above-mentioned simple dovetailer to all computable universes. His widely known writings come out on top when you google for "computable universes" etc, so Wolfram must have known them too, for he read an "immense number of articles books and web sites" (page xii) and executed "more than a hundred thousand mouse miles" (page xiv). He endorses Schmidhuber's "no-CA-but-TM approach" (page 486, no reference) but not his suggestion of using Levin's asymptotically optimal program searcher (1973) to find our universe's code.
On page 469 we are told that the simplest program for the data is the most probable one. No mention of the very science based on this ancient principle: Solomonoff's inductive inference theory (1960-1978); recent optimality results by Merhav & Feder & Hutter. Following Schmidhuber's "algorithmic theories of everything" (2000), short world-explaining programs are necessarily more likely, provided the world is sampled from a limit-computable prior distribution. Compare Li & Vitanyi's excellent 1997 textbook on Kolmogorov complexity.
On page 628 ff we find a lot of words on human thinking and short programs. As if this was novel! Wolfram seems totally unaware of Hutter's optimal universal rational agents (2001) based on simple programs a la Solomonoff & Kolmogorov & Levin & Chaitin. Wolfram suggests his simple programs will contribute to fine arts (page 11), neither mentioning existing, widely used, very short, fractal-based programs for computing realistic images of mountains and plants, nor the only existing art form explicitly based on simple programs: Schmidhuber's low-complexity art.
Wolfram talks a lot about reversible CAs but little about Edward Fredkin & Tom Toffoli who pioneered this field. He ignores Wheeler's "it from bit," Tegmark & Greenspan & Petrov & Marchal's papers, Moravec & Kurzweil's somewhat related books, and Greg Egan's fun SF on CA-based universes (Permutation City, 1995).
When the book came out some non-expert journalists hyped it without knowing its contents. Then cognoscenti had a look at it and recognized it as a rehash of old ideas, plus pretty pictures. And the reviews got worse and worse. As far as I can judge, positive reviews were written only by people without basic CS education and little knowledge of CS history. Some biologists and even a few physicists initially were impressed because to them it really seemed new. Maybe Wolfram's switch from physics to CS explains why he believes his thoughts are radical, not just reinventions of the wheel.
But he does know Goedel and Zuse and Turing. He must see that his own work is minor in comparison. Why does he desparately try to convince us otherwise? When I read Wolfram's first praise of the originality of his own ideas I just had to laugh. The tenth time was annoying. The hundredth time was boring. And that was my final feeling when I laid down this extremely repetitive book:exhaustion and boredom. In hindsight I know I could have saved my time. But at least I can warn others.

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Fundamentals Of Multibody Dynamics. Theory And Applications Review

Fundamentals Of Multibody Dynamics. Theory And Applications
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Sadly the reviewer below DO not seem to be technically competent enough to understand that the theory of multi-body dynamics is actually the implementation as multibody formulations are an approach suitable for computational software such as Matlab (with the help of its symbolic toolbox).
There are some small typos, but honestly they do not matter much and are not more then other texts. I was compelled to write a review as I realized the immense value that this book offers for beginners to learn advanced engineering dynamics involving complex multibody systems, suitable for self study. All you need a paper and pen as well as matlab etc to implement your multibody systems of equation, as books leads you from beginning to advanced level required for research or industry.
Now about the BOOK.
This book outline the most systematic approach to model complex multibody systems from kinematics, kinetics to flexible multibody systems in an amazingly comprehensive way. Honestly it is much more comprehensive then Shabana's book and its use of arrays to represent kinematics of open/closed looped multibody system is readily suitable for computational implementations.
Here what the book does.
It begins with where the traditional "dynamics of mechanical systems" such as that covered by Dr. Ginsberg's book. It then outlines a systematic process to represent kinematic of complex multibody systems. Later on rigorous derivation equations of motion using various approaches, along with full chapter to derive generalized forces and a full chapter to represent constraints follows. What's more, the chapter on finite element modeling of flexible multibodies is by far the most comprehensive that there is. Finally a chapters on boundary element method and on modeling of multibody systems involving "flexible terminal ends" is just as useful (say for robotics).
My aim was to learn and to be able to model arbitrary complex helicopter rotor systems using multibody dynamics. This book provided the perfect foundational engineering dynamics knowledge.
I am in no way associated with author (I don't even know him). This book is ideal for self study (as I did). I hope this review helps as this book is immensely valuable.

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Geometric Control of Mechanical Systems: Modeling, Analysis, and Design for Simple Mechanical Control Systems Review

Geometric Control of Mechanical Systems: Modeling, Analysis, and Design for Simple Mechanical Control Systems
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This is a very well written book on a difficult subject. A thorough study of "simple mechanical control systems" needs a background in Differential Geometry and students are faced with a great challenge in finding good references. Lewis and Bullo take that challenge and deliver this profoundly useful text. This book provides excellent chapters on the relavent concepts in basic algebra, differential geometry and Lie algebra. Then it introdues the simple mechanical systems in a very readable way. Concepts such as the configuration manifold, rigid bodies, kinetic energy, Riemannian metric etc are well explained in a general setting that the reader can draw analogies to Newtonian mechanics in Euclidean space. Book further discusses more advance topics such as Stability, Controllability and Perturbation analysis etc in Part II. Part III discusses design methodologies. There are plenty of exercises in the book and the website is very resourceful with supplementary material. This is a great addition to the collection of books by Abraham, Marsden, Arnol'd etc, and a must have for all graduate students working on this area.

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Modeling of Oil Product and Gas Pipeline Transportation Review

Modeling of Oil Product and Gas Pipeline Transportation
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This books is probably one of the best I've ever read in the field of Hydraulic Design. It has a comprehensive, clear and consistent aproach. The math behind the deductions was aimed to be understood by an engineer/practisioner. In my opinion is a must have in anyone's library.

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Biological Modeling and Simulation: A Survey of Practical Models, Algorithms, and Numerical Methods (Computational Molecular Biology) Review

Biological Modeling and Simulation: A Survey of Practical Models, Algorithms, and Numerical Methods (Computational Molecular Biology)
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If you want to have working knowledge (with theoretical background) but do not have much time to take all related classes, then this book should be a good place to start. Exposition of concepts is akin to real biological problems. Many pseudo-codes are directly implementable within one or two hours. I recommend this especially for those who are not familiar with scientific programming since it teaches how to approach scientific problems. Although the book is meant to summarize related methods but each section covers enough details with clear explanation.

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A practice-oriented survey of techniques for computational modeling andsimulation suitable for a broad range of biological problems.

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Mathematical Modeling of Physical Systems: An Introduction (Engineering & Technology) Review

Mathematical Modeling of Physical Systems: An Introduction (Engineering and Technology)
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This book has a really nice, diversified selection of topics, and is quite readable. However, out of the three parts I looked at carefully, it is clear that one (the example on Global Positioning System) has a mathematical mistake (equations 3.13a,b,c), when solving a set of equations simultaneously in which all reference to y and z squared terms was lost on the right side of the equation. I think the book does well in explaining the big ideas, but one had better check all the details of the calculations before accepting them.

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Mathematical Modeling of Physical Systems provides a concise and lucid introduction to mathematical modeling for students and professionals approaching the topic for the first time. It is based on the premise that modeling is as much an art as it is a science--an art that can be mastered only by sustained practice. To provide that practice, the text contains approximately 100 worked examples and numerous practice problems drawn from civil and biomedical engineering, as well as from economics, physics, and chemistry. Problems range from classical examples, such as Euler's treatment of the buckling of the strut, to contemporary topics such as silicon chip manufacturing and the dynamics of the human immunodeficiency virus (HIV). The required mathematics are confined to simple treatments of vector algebra, matrix operations, and ordinary differential equations. Both analytical and numerical methods are explained in enough detail to function as learning tools for the beginner or as refreshers for the more informed reader. Ideal for third-year engineering, mathematics, physics, and chemistry students, Mathematical Modeling of Physical Systems will also be a welcome addition to the libraries of practicing professionals.

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A First Course in Scientific Computing: Symbolic, Graphic, and Numeric Modeling Using Maple, Java, Mathematica, and Fortran90 Review

A First Course in Scientific Computing: Symbolic, Graphic, and Numeric Modeling Using Maple, Java, Mathematica, and Fortran90
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Landau takes a refreshingly different approach to teaching students scientific computation. The field can be considered as two parts. One, the older and more heavily used, is about the "traditional" numerical analysis. You crunch numbers, and you get numbers out. The other approach is symbolic algebra.
Usually a text only deals with one type. Here, he teaches both. Plus, for each type, he offers the choice of two languages. For the numerical analysis, there is Fortran, version 90, and Java. While the symbolic algebra is performed using Mathematica or Maple. Ecumenical indeed!
These are excellent choices of languages. Fortran still dominates legacy numerical analysis, with massive libraries of subroutines that one has to work with or maintain. While Java lets the student learn good object oriented practices.
And Mathematica and Maple are perhaps the most common symbolic packages available.

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This book offers a new approach to introductory scientific computing. It aims to make students comfortable using computers to do science, to provide them with the computational tools and knowledge they need throughout their college careers and into their professional careers, and to show how all the pieces can work together. Rubin Landau introduces the requisite mathematics and computer science in the course of realistic problems, from energy use to the building of skyscrapers to projectile motion with drag. He is attentive to how each discipline uses its own language to describe the same concepts and how computations are concrete instances of the abstract.

Landau covers the basics of computation, numerical analysis, and programming from a computational science perspective. The first part of the printed book uses the problem-solving environment Maple as its context, with the same material covered on the accompanying CD as both Maple and Mathematica programs; the second part uses the compiled language Java, with equivalent materials in Fortran90 on the CD; and the final part presents an introduction to LaTeX replete with sample files.

Providing the essentials of computing, with practical examples, A First Course in Scientific Computing adheres to the principle that science and engineering students learn computation best while sitting in front of a computer, book in hand, in trial-and-error mode. Not only is it an invaluable learning text and an essential reference for students of mathematics, engineering, physics, and other sciences, but it is also a consummate model for future textbooks in computational science and engineering courses.

A broad spectrum of computing tools and examples that can be used throughout an academic career
Practical computing aimed at solving realistic problems
Both symbolic and numerical computations
A multidisciplinary approach: science + math + computer science
Maple and Java in the book itself; Mathematica, Fortran90, Maple and Java on the accompanying CD in an interactive workbook format


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Introduction to Mathematical Modeling Using Discrete Dynamical Systems Review

Introduction to Mathematical Modeling Using Discrete Dynamical Systems
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This book introduces Mathematical Modeling without requiring a background in differential equations. I think it does so effectively, although I agree with the previous reviewer that some of the examples are trivial. If used as part of a college course, the instructor really ought to assign computational projects.
Some of the models presented in the book are very similar, but that is not an issue with the book. It seems like an effective way to get students to figure out on their own how similar models are connected. In later chapters, function families are introduced that show how the behavior of interest can be analyzed more generally. Working with pencil and eraser, I prefer easy models. In practical applications, it's all going to be done by a computer anyway.
There are other Mathematical Modeling books that will be more challenging. However, introducing these concepts in ways that require differential equations and linear algebra may just make it more difficult to focus on the new concepts. If you understand the ideas in these simple models, you will also recognize them in more advanced models.

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MATHEMATICAL MODELING USING DISCRETE DYNAMICAL SYSTEMS! This mathematics text introduces powerful mathematical modeling techniques while providing you with the tools you need to succeed. Exercises with answers, suggested computer projects with specific instructions for their completion, and the book-specific website are just a few of the tools that will help you master the material. Coverage of current research, such as dynamical systems, shows you that mathematics is a vibrant and evolving discipline.

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