Product Description

This book details the necessary numerical methods, the theoretical background and foundations and the techniques involved in creating computer particle models, including linked-cell method, SPME-method, tree codes, amd multipol technique. It illustrates modeling, discretization, algorithms and their parallel implementation with MPI on computer systems with distributed memory. The text offers step-by-step explanations of numerical simulation, providing illustrative code examples. With the description of the algorithms and the presentation of the results of various simulations from fields such as material science, nanotechnology, biochemistry and astrophysics, the reader of this book will learn how to write programs capable of running successful experiments for molecular dynamics.

Product Description

With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.

Customer Reviews (10)

Excellent Text and Reference!
As an intermediate Python programmer, this excellent book has become my go to reference for useful intermediate and advanced techniques that I can locate and learn quickly. The writing is clear and not overly verbose. In addition to a wide array of numerical and scientific examples, the book is helpful for a wide range of programming issues, such as gluing together disparate legacy applications, interfacing to C++, regression testing numerical code, building GUI's, web programming, etc.

Outstanding introduction to Python and Numpy
I've bought what seems to (my wife) be every Python book out there and I can't tell you how sick I am of spam, spam, spam code! (trivial and obfuscated Python code examples with a common theme focused around one Monty Python skit or another...) Spam code seems to prevail in other Python books.

Here finally is a book with code examples that are very clear, are immediately useful to the serious programmer and filled with real life discourse on relative performance differences between Python and other languages that have a reputation for speed.There are clear examples of 'number crunching', producing images and even video animations, hooks into other scientific packages such as MathLab, etc.

If you are interested in really learning Python, want to come away from an hour or twos worth of coding experience with a module or two that you can use tomorrow and are not interested in code examples extolling Monty Python silliness, then this is the book for you.

While this book is about twice as expensive as many of my other Python books, I wish I had purchased this one first.Even though I've been using Python, seemingly every day, for two years, I kept finding nuggets in this book with what seemed to be every turn of the page.My focus right now is processing extremely large data sets of binary data but I'll soon be looking at image processing and I know I'll be reaching for this book over and over again.Don't hesitate!Just buy the book!

*The* reference for folks who work with Matlab
I'm giving this book five stars because it was basically written for me.I don't mean that literally, of course.I say that because the usual methods of googling for answers and reading the manual do not work when you are trying to push the limits of what a tool is capable of doing.I do numerical computations for a variety of things -- finding patterns in large data sets, automating data collection and analysis, converting raw serial output into convenient CSV, plotting multidimensional datasets etc.Over the years, I have collected a large number of productivity habits with Matlab, which allows me to do ridiculously convoluted things in a short period of time.You just have to read the introduction of any Python manual to understand why I am switching from Matlab to Python.The problem is -- what will replace all these productivity habits?They need to be replaced with "Pythonic" habits, something that can take years of practice.

The beauty about Langtangen's book is that it runs through every one of those techniques.Instead of giving a basic example (what your google search would have provided) or a complete list of, ahem, useless techniques (what the manual would have provided), you get exactly what a seasoned data analyst needs to know to get moving with state-of-the-art commands.The author also discusses optimizations and alternatives in each chapter.

The book is also the best source for explaining *why* NumPy should be used by people working with large datasets.Folks love to create toolkits for Python, but some of these are a list of non-intuitive shortcuts that don't provide a substantial improvement over basic Python.Langtangen goes through the pain of explaining the benefits of the package (chapter 4.1.4), so that you can decide for yourself if NumPy is useful for your application.

I will not comment on the parts of the book that deal with C and FORTRAN integration because I leave that to more able programmers.I also will not comment on the extensive GUI building chapters because I do not build GUIs.I will point out, though, that I have derived full value out of this book simply by reading, and re-reading chapters 2, 3, 4 and 8.Some will argue that there is too much "basic Python" in these chapters for the whole to be considered advanced computational science -- my opinion is that even when the author describes "basic Python", his examples and intuition make it so that even one who has read a couple of reference books cover-to-cover will learn something about using "basic Python" to perform numerical analysis in a more efficient way.In fact, the book is a testament to doing really convoluted things in a really compact and elegant manner!

Absolutly Outstanding
Python Scripting for Computational Science is both an introduction to the Python language and an excellent reference for the intermediate developer.The approach taken by the author is to present the language in the form of tasks to be solved accompanied by example code.As expected for a book on scientific computing the modules covered in the examples emphasize numerical packages but this in no way detracts from the applicability to general Python enthusiast.

What really makes this book more than just another Python introduction is that the author bridges the gap between complied and interpreted code.He demonstrates how the speed of execution of compiled code can be tied to the rapid pace at which scripts can be developed.Examples are provided for interfacing C, C++ and FORTRAN code with Python.Calls to precompiled applications are also covered and the examples were easily adapted to my favorite computational tools.One of the risks with doing numerical work in a scripting language is the possibility of straying into computationally intensive tasks to which interpreted code is not well suited .Latter chapters discuss how to identify these portions of your code and how to migrating these tasks to a compiled language.

good book
If you want to learn Python, you should get it. Author do not build some "big" application (like "internet store software" or "bookstore software") from beginning to end, but rather give you a lot of practical examples of using python.

It is not like in others book that examples include only learned functions/methods, but use topics from the rest of book (you have example on page 25 and note that explanation of this and that function you found on page 543). By that you have interesting examples to use in real-world problems, not only examples to explain freshly learned topic.
In other books interesting examples of use python you found on page 3234, because only when author introduce all useful functions. In this book nice examples is even on first pages.


You learn how to use numerical packages (numpy) in python, using some useful tricks on lists and arrays, introduce to using graphical interface in Tk. ... Read more

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This book presents the full range of computational science and engineering -- the equations, numerical methods, and algorithms with MATLAB® codes.The author has taught this material to thousands of engineers and scientists.The book is solution-based and not formula-based: it covers applied linear algebra and fast solvers, differential equations with finite differences and finite elements, Fourier analysis, optimization, and more.

ContentsChapter 1: Applied Linear Algebra; Chapter 2: A Framework for Applied Mathematics; Chapter 3: Boundary Value Problems; Chapter 4: Fourier Series and Integrals; Chapter 5: Analytic Functions; Chapter 6: Initial Value Problems; Chapter 7: Solving Large Systems; Chapter 8: Optimization and Minimum Principles. ... Read more

Customer Reviews (6)

Not as good as his linear algebra book
Even though I love Prof. Strang for making his lectures available for people like me and even though I liked his linear algebra book, I can not say that this book is excellent.I think that he puts too many topics in one book without giving enough details or depth on any of them.The book is more like a collection of lecture notes rather than a book.If you like to study linear algebra, use his linear algebra book.If you study finite difference method for PDEs, Hoffman's is a much better choice.For numerical linear algebra, there is the book by Trefethen.

So, my suggestion for you is as follows.Pick up the book from a library.And go straight to a topic that you are somewhat familiar with.And try to see if Prof. Strang does an excellent job on that topic.I did that for a few of them, and I found that the book is not sufficient explaining those topics.

By the way, for CG method neither the book nor the video lectures are very useful.You will need the article by Jonathan Richard Shewchuk.You can find it online.This is the best for CG.

The best
This book has a modest amount of information on a whole range of topics in applied mathematics. The authors writing style makes it a lot easier on the eyes than other textbooks in the field. I recommend it to anyone who is interested in numerical methods.

Excellent for making connections, but not a text book
This books gives a unified view to topics which can seem unrelated.
For someone who has prior knowledge in CSE or applied mathematics, this book is good. Keep it in your shelf and read it to enjoy the beauty of the subject, not to learn from it.

This book is not any where close to Strang's linear algebra book.

absolutely recommended
This book is a must in bring all the main notions of scientific and engineering computation and establishing the links between them in a very comprehensive manner. Unmissable book for who is interested in the topic.

A Great Book for Mathematicians and Engineers
The following is the review I published in The UMAP Journal (Summer, 2009, Vol 30, no. 2) pp. 175-178.

My second review for this journal [1986] was of Gilbert Strang's Introduction to Applied Mathematics (hereafter IAM). I have never been too happy with that review, where I said that it is a "wonderful book." True enough; but more appropriately, it is an important book, as is the book reviewed here, Computational Science and Engineering (hereafter CSE).

CSE is--and is not--a second edition of IAM. Apparently, it is the result
of more than 20 years of Strang teaching his favorite course at MIT,
presumably out of IAM. Since CSE does not contain everything in IAM
and also contains topics not in IAM, it is a different text. CSE contains
Strang's further ruminations on the nature of applied mathematics, and
I view it as the superior text, but some individuals might prefer IAM. To
some extent, either book represents Strang's philosophy of teaching applied mathematics--that we need a new approach--but this conviction is much more explicit in CSE.

In particular, Strang believes that we should focus on both modeling and
computation. Many books are about one or the other, and he feels that applied mathematics is both. Furthermore, Strang believes that applied problems tend to have a common structure, and Chapter 2 is devoted to illustrating this principle through a wide variety of problems.

In my review of IAM, I tried to give an idea of the range of topics without enumerating the contents. CSE has the same difficulty: Enumerating the topics is tedious, but the titles of the chapters are informative (though listing them does not do justice to the sheer range of content):
1. Applied Linear Algebra
2. A Framework for Applied Mathematics
3. Boundary Value Problems
4. Fourier Series and Integrals
5. Analytic Functions
6. Initial Value Problems
7. Solving Large Systems
8. Optimization and Minimum Principles

Strang suggests that a course designed out of this text might follow the
structure that he uses (p. v):
* Applied linear algebra
* Applied differential equations
* Fourier series

I have long been a champion of Strang's books. I have reviewed different
editions of two texts on linear algebra, making clear that that I think he is the most influential author in linear algebra in the last 50 years. I have heaped high praise on his calculus text in my recent editorial on calculus [Cargal 2008]. I have done this for the exact reason that I have championed John Stillwell's books on geometry and algebra. These two authors, as well as a handful of others, write with authority leavened with the great enthusiasm of the born teacher. They are superb pedagogues.

What makes IAM and CSE so important is that they cover a great deal
of applied mathematics, and there is nothing in the literature that compares to them. Pedagogical works, as opposed to dry tomes, are simply
rarer in applied mathematics than they are in, say, calculus, linear algebra,geometry, and number theory. There are pedagogical works in differential equations and probability. But there is nothing that covers so much applied mathematics as these with comparative pedagogical skill and acumen.

Like IAM, CSE has a long first chapter that is a summary of applied linear
algebra (86 pp in IAM, 97 pp in CSE). Linear algebra is a key to applied
mathematics; it is the most important tool after calculus (this apparently is Strang's view). However, the first chapter is definitely a review. The reader needs to have had a course in linear algebra as well as the usual course in differential equations. These things are minimal. Courses in probability,numerical analysis, and so on certainly help. Knowledge of physics is a definite plus. These days, there are students of applied mathematics(computer science, statistics, operations research) who are physics-phobic. They would have problems with parts of the book. This necessity of a modicum of prior knowledge of applied mathematics means that the level of the book is for seniors and graduate students. The online comments about IAM are striking in their simplicity: Students who are not prepared despise the book, the others are enamored with it; there is no middle ground. The reader who is prepared should love this book. In particular, engineers and physicists should love this book.

People in industry, too, should love this book. Mathematicians and
engineers in industry benefit particularly froma book such as this for a very simple reason. Mathematicians in academia tend to specialize because of the need to publish. However, mathematicians in industry are motivated
to generalize. They don't have tenure; often they depend on contracts, so
that specializing can limit opportunities to get work. If a book like CSE (or AIM) had been available when I went into industry more than 30 years ago,it would have changed my life; it certainly would have made those first years easier. In fact, one topic that Strang covers very nicely in both books is the Kalman filter, a topic that is very big in industry and that occupied me in my first job.

The most important thing I tell my students is the need to study if they go into industry. This is particularly true if the student has stopped at the bachelor's degree, since a bachelor's degree is essentially a learner's permit.Few students go to work for national labs (those who do, do not need my advice--I need theirs), which means that on-the-job training is unlikely or superficial. Of people who have technical degrees, only a small portion maintain their technical skills; most simply travel along and forget much of what they learned. People tend to learn or they forget; nobody remains in stasis. In industry, you should take some of your time on the job to study.

Is spending work time studying material that is not clearly work related
to the work unethical? Typically, doing so does not create a problem (as
long as one gets one's tasks done). However, if your supervisor sees you
reading a newspaper, that could create a problem. On the other hand, if
you are studying number theory, there is no problem; that number theory
has nothing to do with your current job tasks will almost certainly not
register. Moreover, the worker who studies number theory will tend to
retain competence in differential equations far better than a worker who
just lets technical skills dissipate. In fact, those few workers who develop good technical reputations almost always study widely while on the job. Their ability to quickly respond to new problems on the job is a result of having used work time not to do company tasks. I view this behavior as a survival skill. The fact is, if one "steals" company time to study mathematics and engineering--even topics that have nothing to do with the job--one is far more likely to be promoted because of it than to be reprimanded.

However, the young worker almost always would benefit not only from
learning more number theory but--more urgently--needs to learn a lot
more applied mathematics. The undergraduate curriculum can't cover it
all. Key core areas are not just physics and differential equations, but probability,numerical analysis, and programming. For a worker in industry, CSE would be invaluable, and yet experienced engineers and mathematicians will also be impressed by this book.

Computational Science and Engineering should be in the library of every
applied mathematician, not to mention engineers. As a textbook, it is well suited for a senior or graduate course in applied mathematics.

References
Cargal, J.M. 1986. Review of Strang [1986]. The UMAP Journal 7 (4): 364-
365.

Cargal, J.M. 2008. Calculus: Textbooks, aids, and infinitesimals. The UMAP
Journal 29 (4): 399-416.

Strang, Gilbert. 1986. Introduction to Applied Mathematics. Wellesley, MA:
Wellesley-Cambridge Press.

[...] ... Read more

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Customer Reviews (1)

A great college book
This is a great book for the college bound. Very interesting and easy to read. ... Read more

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Presenting a practical, problem-based approach to colour physics, this title describes the key issues encountered in modern colour engineering, including efficient representation of colour information, fourier analysis of reflectance spectra and advanced colorimetric computation. Emphasis is placed on the practical applications rather than the techniques themselves, with material structured around key topics, such as colour calibration of visual displays, computer recipe prediction and models for colour-appearance prediction.

Each topic is carefully introduced at three levels to enhance student understanding. Firstly, theoretical ideas and background information are discussed, explanations of mathematical solutions then follow and finally practical solutions are presented using MATLAB.

• Includes a compendium of equations and numerical data required by the modern colour and imaging scientist.
• Numerous examples of solutions and algorithms for a wide-range of computational problems in colour science.
• Provides example scripts using the MATLAB programming language.

This text is a must-have for students taking courses in colour science, colour chemistry and colour physics as well as technicians and researchers working in the area.

  ... Read more

Customer Reviews (1)

Better to buy a book on color science and learn MATLAB?
I think this book appeals to people who don't want to or have time to code in MATLAB. It does not have in depth information on color science, so you will want to buy a separate book. Once you have done that, you can just as easily write your own code; the code involved in color science is not that complex. The value in borrowing code is if it is concise and fast (not saying theirs isn't), or if it saves you effort. Since my background was lacking, I ended up reading Schanda's Colorimetry and cranking out my own code to complement MATLAB's Image Processing Toolbox. ... Read more

Product Description
Computational Mathematics in Engineering and Applied Science provides numerical algorithms and associated software for solving a spectrum of problems in ordinary differential equations (ODEs), differential algebraic equations (DAEs), and partial differential equations (PDEs) that occur in science and engineering. It presents detailed examples, each including a complete analysis of a computer code written in transportable Fortran 77. Each example also includes a discussion of the problem equations, the coding of the equations, and the computed numerical solution. The benefits of using quality general-purpose library routines to solve ODE/DAE/PDE problems are illustrated as well. This popular, classicbook is a valuable reference for methodologies in numerical mathematics applicable to a broad spectrum of problems encountered across many disciplines- virtually all fields of science and engineering. It also serves asan excellent text for senior undergraduates or beginning graduate students in computational science. ... Read more

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This book covers modern approaches to Verification andValidation of computer codes, and other topics related toQuantification of Uncertainty and code Quality Assurance. Especiallynoteworthy are the Method of Manufactured Solutions, a general methodfor obtaining exact solutions for use in code Verifications, and theGrid Convergene Index, a method for uniform reporting of gridconvergence tests. The methods and issues are applicable to commercialcodes as well as to in-house developed research codes, and to allproblems modeled by partial differential equations; examples includeComputational Fluid Dynamics, groundwater flow and transport modeling,electrodynamics, etc. The book provides useful formulas and practicalguidance for error estimation and for experimental Validation. ... Read more

Customer Reviews (1)

single reference to cover a broad range of V&V
This book looks great.

You will think "How incredible! How a book can give a set of well-balanced information to coverboth practical and engineering based point of view about verification and validation in a real world? ... Read more

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Nuclear engineering has undergone extensive progress over the years. In the past century, colossal developments have been made and with specific reference to the mathematical theory and computational science underlying this discipline, advances in areas such as high-order discretization methods, Krylov Methods and Iteration Acceleration have steadily grown.

Nuclear Computational Science: A Century in Review addresses these topics and many more; topics which hold special ties to the first half of the century, and topics focused around the unique combination of nuclear engineering, computational science and mathematical theory. Comprising eight chapters, Nuclear Computational Science: A Century in Review incorporates a number of carefully selected issues representing a variety of problems, providing the reader with a wealth of information in both a clear and concise manner. The comprehensive nature of the coverage and the stature of the contributing authors combine to make this a unique landmark publication.

Targeting the medium to advanced level academic, this book will appeal to researchers and students with an interest in the progression of mathematical theory and its application to nuclear computational science.

Product Description
This is a classroom-tested and developed textbook designed for use in either one- or two term courses in Partial Differential Equations taught at the advanced undergradute and beginning graduate levels of instruction. In addition to covering the standard PDE topics taught in such courses, there is a heavy focus on vector analysis. Maple examples and exercises for the students are presented strategically within the book, and an Appendix on Maple facilitates the use of this computer algebra language, acting as a quick reference source for readers. In addition, a cross-platform CD-ROM which is packaged with the text contains many analytical examples for students in the use of Maple. However, despite its general computational orientation, this book is designed so that it can also be readily used by instructors not wishing to bring the computational aspects of a computer algebra system into the classroom. This book will have strong appeal to interdisciplinary audiences! of engineers and scientists, particulary in regard to its treatments of fluid mechanics, heat equations, and continuum mechanics, inclusion of the latter being a unique feature of the presentation. ... Read more

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Newer computer architectures rely on multi-core, multi-chip designs to achieve the highest performance; programmers, therefore, need to utilize multi-threading and parallel programming techniques in their applications to achieve high performance on these new designs. This book provides application developers with a detailed understanding of how to effectively program for these new high performance architectures. The authors give a broad overview of the current state of hardware and software advances to support high performance applications. They cover application optimization for hybrid multi-core architectures and focus on the more common and successful strategies for multi-threading and parallel programming using examples from actual codes.

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A complete desk-top reference for working scientists, engineers, and students, this handbook serves as a veritable math toolbox for rapid access to a wealth of mathematics information for everyday use in problem solving, examinations, homework, etc. Compiled by professional scientists, engineers, and lecturers and internationally renowned for its clarity and completeness, The Handbook includes hundreds of tables of frequently used functions, formulae, transformations, and series, plus many applications. The layout, structured table of contents, and index make finding the relevant information quick and painless. ... Read more

Customer Reviews (13)

Handbook of Mathematics and Computational Science

Harris and Stocker's Handbook of Mathematics and Computational Science is a reference book that many readers learning or reviewing calculus would appreciate. It is not a dictionary or encyclopedia of mathematics, nor any other sort of collection of articles, but rather a vast compendium of descriptions and formulas from the non-theory portion of the undergraduate mathematics curriculum. The information is sometimes quite compressed. Although chapter two does list the axioms for a group, a ring, a field, and a vector space, there are no further references to these subjects.The focus of the handbook is non-theoretical. A listing of the chapter titles will help clarify this.

1. Numerical computation (arithmetic and numerics) .. 1
2. Equations and inequalities (algebra) .. 37
3. Geometry and trigonometry in the plane .. 59
4. Solid geometry .. 95
5. Functions .. 117
6. Vector analysis .. 331
7. Coordinate systems .. 349
8. Analytic geometry .. 377
9. Matrices, determinants, and systems of linear equations ..409
10. Boolean algebra-application in switching algebra .. 467
11. Graphs and algorithms ..483
12. Differential calculus .. 489
13. Differential geometry .. 517
14. Infinite series .. 531
15. Integral calculus ..547
16. Vector analysis .. 591
17. Complex variables and functions .. 613
18. Differential equations .. 647
19. Fourier transformation .. 691
20. Laplace and z transformations .. 735
21. Probability theory and mathematical statistics .. 773
22. Fuzzy logic .. 847
23. Neural networks .. 871
24. Computers .. 883
25. Table of Integrals .. 951
Index .. 999

Handbook of mathematics and computational science
Handbook of Mathematics and Computational Science
This is a good book and it can be used in conjunction with Handbook of mathematical functions (by M. Abramowitz and I. Stegun)

Good for a refresher reference
I use this book a lot when I forget those little bits of mathematical wonder that are necessary in only the most obscure of my physics problems, and for that it has been well worth the money. However, I haven't used, and really don't intend to use, any of the text on fuzzy logic and...is there actually FORTRAN programming in here? Wow...

Useful day to day
For a useful 'everyday' collection of the math regularly needed for engineering calculations, this is just great.

emphasises computational aspects
If you are a professional programmer, this handbook can be useful for its coverage of maths used in computing. The emphasis is on discrete maths. With long discussions on Boolean logic and graph theory. The book could be compared to Knuth's Art of Computer Programming or with Numerical Recipes. Not as advanced as the former, but more indepth than the latter in some aspects.

The book gives a nice explanation of neural networks and fuzzy sets. These are important ideas that programmers should be acquainted with. In earlier years, they were more like pure research topics. But their success has led to increasing usage in computing, so it is good to understand them. ... Read more

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The volume is based on the papers that were presented at the international conference Model-Based Reasoning in Science and Technology (MBR09_BRAZIL), held at the University of Campinas (UNICAMP), Campinas, Brazil, December 2009. The presentations given at the conference explored how scientific cognition, but several other kinds as well, use models, abduction, and explanatory reasoning to produce important or creative changes in theories and concepts. Some speakers addressed the problem of model-based reasoning in technology, and stressed the issue of science and technological innovation. The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in logic, science, and technology: the most recent results and achievements about the topics above are illustrated in detail in the papers. The book is divided in three parts, which cover the following main areas: part I, abduction, problem solving, and practical reasoning; part II: formal and computational aspects of model based reasoning; part III, models, mental models, representations. ... Read more

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Customer Reviews (6)

An important collection
The first things you need to know about this book is that it is a collection of articles. This is not a bad thing, unless you know nothing of the subject. Beginners should start somewhere else, and if they are still interested then they should definitely get this volume. The articles are all well written, although some ideas were redundant. The author includes a short introduction to each section and this helps in creating a coherent picture. The CD is a great addition. It's quite interesting looking at the different simulations and playing around with the numbers. This shows how powerful the idea of agent based simulation is. My favorite part was the first couple of papers where the author states the advantages of computer modeling in the social sciences. All in all, this is an excellent volume but it's not for everybody.

Good summary of work following Growing Artificial Societies
Epstein pulls together great examples from the use of ABS to grow or identify rules associated with behaviors and their manifested group behaviors.

The first few chapters give a great summary of Epstein's Generative thinking.A good fountain for any scientist who is interested in using Agent Based Simulation.

Instead of Can You Explain It, Can You Build It?
Sometimes I encounter books that are extremely important, that give me an appreciation for a knowledge domain I do not know enough about, and that I simply cannot read and review in the traditional sense.However, having invested good money and time in the book, if I admire I book, I generally seek to use my broad reading as a base for putting the book in an appreciative context with useful links for other readers.

This book, and Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity) are two such books.This one starts with:

"instead of explaining it, can you grow it?"

Howard Bloom, in Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century teaches us that the only way to create a sustainable peace in the Palestine region is to provide absolute security for an entire generation, and raise two whole generations, one on each side, from kindergarten on us, generations that do not consider "the other" to be "pigs and monkeys" by the age of five.

Similarly, the literature on wealth of networks and the fortune at the bottom of the pyramid is growing, and I am convinced that public intelligence (decision support, full disclosure, end of information asymmetries) is going to accomplish two things in the next twenty years:

1) Eradicate corruption and enforce the triple-bottom line

2) Elevate five billion poor by teaching them one cell call at a time so that they can create infinite stabilizing wealth.

See for example:
Infinite Wealth: A New World of Collaboration and Abundance in the Knowledge Era
The Wealth of Networks: How Social Production Transforms Markets and Freedom
Revolutionary Wealth: How it will be created and how it will change our lives
The Fortune at the Bottom of the Pyramid: Eradicating Poverty Through Profits (Wharton School Publishing Paperbacks)

So the very best thing I can say about this book is that I am glad I bought it, I am very glad to have a sense, however weak, of this important exploratory area, and now I know that I need a team of generative social scientists that can do complex modeling for peace and prosperity solutions.

See also, just published at Amazon and free online at Earth Intelligence Network, Collective Intelligence: Creating a Prosperous World at Peace

I urge one and all to become familiar with World Index of Social and Environmental Responsibility (WISER), as best I can tell that is the center of gravity for empowering individuals with deep knowledge of the true costs and many human rights abuses and other crimes that we support today for lack of knowledge.I also recommend the pioneering EarthGame work of Medard Gabel, at BigPictureSmallWorld.

Eventually I see the USA Waging Peace, with a Multinational Decision Support Center providing unclassified intelligence to all actors on the world stage, and publishing an annual and constantly updated Global Range of Gifts Table to connect the billion rich with the five billion poor at the $1-$100 level.

In commenting on this book, I am primarily seeking to point readers toward other books on the substance of peace and prosperity and our many ills.If you are technically inclined, this is a very top work that also inspires the lay reader who "does not do math."

Excellent example of cross-disciplinary social science using theory
It's refreshing and exciting, in a quiet intellectual kind of way, to encounter a book that includes philosophy of science, music theory, Anasazi disappearance mysteries, ethnic cleansing, and an explanation of why CEOs exist. Josh has produced the book I've been wanting to read any time during the last 20 years, which have been a bit barren from the theory and modeling perspective in social science. He also makes clear the mathematical and philosophical basis of the agent-based approach, producing a baseline both for future work in the field and for competing paradigms such as systems dynamics, discrete simulations, and cellular automata (Wolfram's New Kind of Science), however incommensurable. I was particularly interested in the occasional use of probability modeling (negative exponential distributions generated through simple rules are a very interesting advance in understanding the waiting times between civil violence outbursts) and I'd love to see a deeper relationship established, say between Bayesian models of dynamic systems and agent-based models. Keep up the great work, Josh! Also, kudos to the publisher for the sheer quality of the book: excellent paper, great color plates, and priced to sell rather than as the work of art it is.

Excellent survey of the author's work
This book did a good job of introducing me to the current state of agent-based modeling. It also, perhaps inadvertently, highlighted some of the current weaknesses of the field. In particular, the models shown in each paper rarely shared common features, and there was little consistency in method.

Epstein argues persuasively that agent-based modeling is a tool, not a methodological approach, and you should no sooner expect consistent usage here than with differential calculus. That said, it was a bit disconcerting.

Also, while the goal espoused here was to use the bare minimum of constraints that retain explanatory power, I was disappointed that relevant work from other fields was often abstracted away. For example, a few models used social networks; but the networks presented were static, not dynamic, and were not built around power-law ratios. Such additional complexity may well have distracted from the main point; but it would have been nice to see at least some discussion of why the models were simplified.

Regardless, I was very pleased with the book and would highly recommend it. ... Read more

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This practical guide describes the basic computational methodologies for catalysis and materials science at an introductory level, presenting the methods with relevant applications, such as spectroscopic properties, chemical reactivity and transport properties of catalytically interesting materials. Edited and authored by internationally recognized scientists, the text provides examples that may be considered and followed as state-of-the art. ... Read more

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This book introduces the concepts and methodologies related to the modelling of the complex phenomena occurring in materials processing. After a short reminder of conservation laws and constitutive relationships, the authors introduce the main numerical methods: finite differences, finite volumes and finite elements. These techniques are developed in three main chapters of the book that tackle more specific problems: phase transformation, solid mechanics and fluid flow. The two last chapters treat inverse methods to obtain the boundary conditions or the material properties and stochastic methods for microstructural simulation. This book is intended for undergraduate and graduate students in materials science and engineering, mechanical engineering and physics and for engineering professionals or researchers who want to get acquainted with numerical simulation to model and compute materials processing. ... Read more

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Modelling at the continuum scale
With the increased power and availability of computers, computer aided modeling is becoming more prevalent in various fields of engineering.One important example is modelling of stress, strain, and flow (plastic solid and liquid) at the continuum level.Examples of this are finite element analysis, finite difference analysis, Monte Carlo simulations, and the random walk. This book covers all of these topics and generally serves as a good introductory textbook to learn about modelling at the continuum scale.The mathematical treatment is very brisk though, and requires prior knowledge of linear and partial differential equations, linear algebra, and basic computing. ... Read more

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Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. 

• A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain
• Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory
• Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience
• Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty

Customer Reviews (2)

From life to mind
Gallistel correctly argues that synapses are too inefficient to act as the "Turing tape" that is necessary for (symbolic) computation, though his reasoning is wrong: the real problem with synapses is that their plasticity interacts, as a result of their extremely close-packing (which is precisely what makes the potentially so useful). This "crosstalk" can undermine sophisticated, quasi-symbolic, synaptic learning. But his proposed "solution", that some unknown new neural storage process analogous to DNA underpins powerful quasi-symbolic brain computations, is pie-in the-sky. Nature is a tinkerer, and it seems much more likely that she has simply patched up the unavoidable defects of synapses using largely ready-made materials. In particular, it's likely, though not proven, that the neocortex is specialised to implement a type of "synaptic proofreading", which allows synapses to act as symbols (see [...]). And the same basic idea, proofreading, also underlies the extraordinarily accurate copying process that underpins Darwinian evolution. So "mind" would be a synaptic version of "life".

An Excellent Critique
The authors' thesis is that since the brain computes, it has to instantiate known information processing and computational principles. They discuss many of these principles and crituque current neuroscience computational assumptions and models as paying insufficient attention to them. They present several biological cases in some detail to illustrate how the current connectionist/neuronet/synaptic weight approach is insufficient to the challenges these cases present. Finally, they they describe specific functions and processes we should be looking for in the brain to explain the behaviors presented in the cases.

It is an excellent book and in the end may warrant its subtitle, "Why Congnitive Science Will Transform Neuroscience." I highly recommend it to anyone who works in neuroscience and especially those who work in computational neuroscience.
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This introduction to computer-based problem-solving using the MATLAB® environment is highly recommended for students wishing to learn the concepts and develop the programming skills that are fundamental to computational science and engineering (CSE). Through a teaching by examples approach, the authors pose strategically chosen problems to help first-time programmers learn these necessary concepts and skills.

Each section formulates a problem and then introduces those new MATLAB language features that are necessary to solve it. This approach puts problem-solving and algorithmic thinking first and syntactical details second. Each solution is followed by a talking point that concerns some related, larger issue associated with CSE. Collectively, the worked examples, talking points, and 300+ homework problems build intuition for the process of discretization and an appreciation for dimension, inexactitude, visualization, randomness, and complexity. This sets the stage for further coursework in CSE areas.

The interplay between programming and mathematics throughout the text reinforces the student s ability to reason numerically and geometrically.

Audience: Undergraduate students whose mathematical maturity is at the level of Calculus I will find this book extremely useful, especially as preparation for further courses in computing and mathematics. It can also be used as a MATLAB reference at any level.

Contents: Preface; MATLAB Glossary; Programming Topics; Software; Chapter 1: From Formula to Program; Chapter 2: Limits and Error; Chapter 3: Approximation with Fractions; Chapter 4: The Discrete versus the Continuous; Chapter 5: Abstraction; Chapter 6: Randomness; Chapter 7: The Second Dimension; Chapter 8: Reordering; Chapter 9: Search; Chapter 10: Points, Polygons, and Circles; Chapter 11: Text File Processing; Chapter 12: The Matrix: Part II; Chapter 13: Acoustic File Processing; Chapter 14: Divide and Conquer; Chapter 15: Optimization; Appendix A: Refined Graphics; Appendix B: Mathematical Facts; Appendix C: MATLAB, Java, and C; Appendix D: Exit Interview; Index ... Read more

Customer Reviews (2)

Excellent Book..great problems
Excellent book. The problems are completely worked out in detail. Unlike other books from high end publishing houses, this book really provides a great educational insight to students on the power of computing for all engineering fields. The authors seem to have worked out most of the problems and examples in their own course at Cornell and they are flawless

Useless
The textbook was useless. Only used it for practice problems, but even then, those were not much help ... Read more

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The present book describes the methodology to set up agent-based models and to study emerging patterns in complex adaptive systems resulting from multi-agent interaction. It offers the application of agent-based models in demography, social and economic sciences and environmental sciences. Examples include population dynamics, evolution of social norms, communication structures, patterns in eco-systems and socio-biology, natural resource management, spread of diseases and development processes. It presents and combines different approaches how to implement agent-based computational models and tools in an integrative manner that can be extended to other cases.

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This book introduces new theoretical techniques in materials research. With the computer power now available, it is possible to use numerical techniques to study various physical and chemical properties of complex materials from first principles. Some typical examples are presented and all the necessary equations and plots are included so that readers can fully understand the details. This book offers the materials scientist access to, and an understanding of the modern development of molecular dynamics and Monte Carlo simulation. It will also be of interest to physicists and chemists engaged in materials research. ... Read more

Customer Reviews (2)

Up to date but not much better than other texts
I read this book to learn more about my field of study; computational materials science.I found it thorough; it covered all the major aspects for modelling of condensed phases at the atomic scale: Monte Carlo, Moleculary Dynamics, statistical thermo, and ab initio methods.

The strong point of this book compared to others on the same subject is that the amount of space devoted to the various subjects closely corresponds to the amount of work done in those subjects.Therefore, a lot of space is spent on empirical and semi-empirical methods.

Density functional theory, which is the workhorse of today's high-end simulations, gets shorted here.Only a couple of pages are spent on it.This contrasts strongly with other books on the same subject, like the books by Raabe, or Catlow, or Finnis, where DFT gets at least a whole chapter to itself.

Likewise, there is not much on electronic structure.There are a fair number of examples in this book, but few are simple enough for the first-timer to follow.The examples tend to show of the abilities of simulations; and not the nuts and bolts of how to do them.

As such, this book is great for an introductory course on computer simulations of materials; assuming the reader has a background in materials science.It is not a good book for a course on DFT, computational quantum mechanics, or solid state calculations.

The Best MODERN treatment: Concise, yet Thorough
With an exponentially growing computational power, more precise and exact treatments of problems of condensed matter physics become affordable, and the dark corners of material science become clearly understandable. At the same time, new methods are becoming developed, that allow for better treatments of various kinds of problems in this field.

This book is a gem, written by three active frontiers on this subject and can be of great value to anyone doing computations in material science. An almost complete and up to date review of the methods used in this field with great lists of references for further studies.

In addition to the workers in this field, I highly recommend this book to anyone who wants to know what happens in the Material Science today. ... Read more