Book Description

Computational science is a quickly emerging field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. However, limited educational materials exist in this field. Introduction to Computational Science fills this void with a flexible, readable textbook that assumes only a background in high school algebra and enables instructors to follow tailored pathways through the material. It is the first textbook designed specifically for an introductory course in the computational science and engineering curriculum.

The text embraces two major approaches to computational science problems: System dynamics models with their global views of major systems that change with time; and cellular automaton simulations with their local views of how individuals affect individuals. While the text is generic, an extensive author-generated Web-site contains tutorials and files in a variety of software packages to accompany the text.

• Generic software approach in the text
• Web site with tutorials and files in a variety of software packages
• Engaging examples, exercises, and projects that explore science
• Additional, substantial projects for students to develop individually or in teams
• Consistent application of the modeling process
• Quick review questions and answers
• Projects for students to develop individually or in teams
• Reference sections for most modules, as well as a glossary
• Online instructor's manual with a test bank and solutions

Book Description

The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.

Customer Reviews (4)

strong computational emphasis
Langtangen's emphasis here is on a reader who comes from a strong background in engineering or science, and is familiar with common computational ideas and has done some programming, but not necessarily in Python. The typical book on Python is aimed at a general programming reader, and the examples in such a book usually are quite elementary, from a computational viewpoint.

The merit of Langtangen's book is that he gets into a lot of computational ideas. This is not a trivial book. Aspects like parsing data in files, connecting to local and remote hosts, and interacting with programs written in other languages are covered. For the latter, the important cases of Fortran and C programs are explained. The choices of these languages is deliberate. In science and engineering, they are the dominant languages for raw computation. And you are likely to have legacy code written in these, that you cannot abandon while using Python.

Great Suppliment to Numerical methods
When I first got ahold of this book I had just finished learning all the gory details of good numerical codes.But when developing tests for simple cases I found that development went way too slow, so someone suggested I learn Python. This book provides a great demonstration of how python can supplement your existing codes.Either by organizing the tests, formatting output, or just adding pretty interfaces.

This book contains a lot of the necessary extras that a scientist or engineer must do to get his work going or finished, which is too pedantic to be taught in most courses.It shows the power of Python over some other scripting languages for this purpose.It is definitely one of the best references on my book shelf.

Python for Science Academics and Engineers, NOT programmers
I bought this book as an experienced programmer and Unix user expecting more of a "Numerical Recepies in Python" emphasis on the efficient implementation of algorithms which happen to be in Python.I should have paid more attention to the description.

This book is really more of a "Grad Student's Guide to Everyday Python Usage".I imagine it would be very valuable to a mathematics Grad student without too much programming or shell experience, looking for an alternative to Matlab.However, there is very little "Computational Science" in this book.Do NOT expect a cookbook of high performance algorithm implementations.

The book is a very verbose 700+ pages, all in an unexcitingacademic LaTeX format.The author works through idiom after idiom for accomplishing different tasks in fairly stand-alone sub-sections without much of a feeling of conceptual "flow" between them.It sort of feels like reading through the author's personal lab notes that he took everytime he learned a new language feature or trick.

If you are an experienced programmer, you will quickly get impatient with the verbose presentation that emphasizes idioms and examples instead of fundamental concepts and syntax reference tables.But, if you are an experienced programmer, you are not the target audience for this book.

Braddock Gaskill

Convincing demonstration of Python's value in science
The author has 2 main goals:
1) To improve the productivity of scientists familiar with specific software systems (especially Matlab, Maple, and Mathematica) by teaching them to "glue" applications together.
2) To advocate Python as the preferred "glue" language. In his own words, "I hope to convince computational scientists having experience with Perl that Python is a preferable alternative, especially for large long-term projects."

He has certainly done a creditable job. As an expert in computational differential equations, he neglects neither efficiency nor correctness, while stressing both simplicity and reliability. In this sense, he has done a great service to the Python community.

The question is: What justifies the purchase of his book?
The answer is: Chapters 4, 9, and 10.

Contents:

1. Introduction--26pp
Very convincing arguments.

2. Getting Started With Python Scripting--38pp
Interesting examples.

3. Basic Python--56pp
A too-quick tutorial. Go to python dot org instead.

4. Numerical Computing in Python--48pp
Stellar explanations of vectorized array operations.

5. Combining Python with Fortran, C, and C++--36pp
Details use of Fortran2Py and SWIG. Mentions many alternatives.

6. Introduction to GUI Programming--70pp
Useful examples of Tkinter/pmw widgets.

7. Web Interfaces and CGI Programming--24pp
Good source of ideas.

8. Advanced Python--132pp
Deep and extensive. Includes: option parsing, regular expressions, data persistence and compression, object-oriented programming, exceptions, generic programming, efficiency.

9. Fortran Programming with NumPy Arrays--32pp
All about efficiency and re-use.

10. C and C++ Programming with NumPy Arrays--40pp
More about efficiency. NumPy C API, C++ objects, and SCXX.

11. More Advanced GUI Programming--73pp
Tedious discussion of both Web and standalone GUIs. BLT, canvas, cgi.

12. Tools and Examples--70pp
Excellent examples of PDE solvers, with a powerful GUI, but quite long and tedious.

A. Setting up the Required Software Environment--16pp
Wonderfully specific installation instructions!

B. Elements of Software Engineering--50pp
Python's strength! Very practical advice on modularity, documentation, coding style, regression-testing, version-control.


Strengths:
+ Downloadable py4cs package, esp. numpytools module
+ Great advice everywhere, e.g. CGI checklist, Pythonic programming, and trouble-shooting.
+ Concrete evidence for most assertions.
+ Very attractive presentation. Sturdy, high-quality cover, binding and pages. Brief, elegant code fragments (except in Chapter 12). Readable prose. No wasted space.
+ Available as 5MB pdf file, after purchase of hardcopy. Very nice.
+ Slides, installation instructions, and errata also at web site. Very professional.


My peeves:
- Not enough tables to be a useful manual.
- On p.428(#7) he points out that handling a raised exception is very slow. However, when I time his example with a positive argument, the try-except version is 20% faster (b/c the if clause is skipped), so he is actually giving bad advice for the general case. Luckily, he contradicts himself later, on page 685: "Exceptions should be used instead of if-else tests." The best advice: Avoid common exceptions in inner loops.
- The 10-page index is not as great as it at first seems. (See Martelli's Python in a Nutshell for a better one.)
- Pure interface functions should 'raise NotImplementedError', rather than 'return'.
- Exceptions should never be trapped mindlessly with 'except:'. That would hide your own SyntaxErrors!
- Too many exercises. (It's published as a textbook.) Since there are no answers, the exercises are useless for non-students. (See Lutz's Learning Python for effective exercises with answers.)


Overall rating:
This contains the best information on numerical programming in Python that I've seen. Though expensive, it could easily be your only Python book, given the excellent online documenation already available. ... Read more

Book Description
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.

  ... Read more

Book Description

"It is the best text of its type that I have come across to date...an excellent resource for anyone involved in mathematical practice up to and including degree standard...it is a pleasant experience to flick through it at leisure, dropping in here and there on some of the thousands of results the book holds. It is beautifully illustrated and set out...we have here a comprehensive volume whose worth will not readily fade with time...If you feel the need to own a mathematical reference to see you through school and university mathematics to graduation, you couldn't do much better than to buy this one." --Mathematics Today

Customer Reviews (11)

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.

Ideal for programmers!
If you're a practicing computer programmer / software developer, you'll probably find this replaces half a dozen old math textbooks on your shelf.And with its extensive breadth, it's sure to cover numerous areas for which you have no references, at all.I've found it to be very clearly written, though quite concise.The density of information is even more impressive, when you consider that it's over 1000 pages!

It's extremely useful, when you just need a quick refresher on some topic or to dig up a common formula.That said, it is a handbook, so it doesn't have a great deal of depth.Obviously, it's not going to be everything to everyone - yet that doesn't mean it won't be quite useful to most!

In summary, I'd say the price makes it a no-brainer.If you're a professional, it will pay for itself very quickly.If not, it still may save you quite a bit of time and frustration, and beats searching the web for answers, almost every time.Over the years, I've continued to marvel at this gem so much that I knew I'd eventually have to write this review!

Useful but limited and *no bibliographies*!
This handbook covers a range of undergraduate applied mathematics.Besides standard topics: geometry, indefinite and definite integrals, linear algebra, complex variables, etc.; it also covers some discrete math: Boolean algebra as applied to digital circuit design, graphs, etc.; and a bit of programming (including written-out programs implementing some algorithms).But its coverage is just too idiosyncratic to rely on.Its treatment of digital logic is surprisingly long, and its treatment of matrices surprisingly short.Why does it mention the rather specialized topics of fuzzy logic and neural networks?Why doesn't it mention wavelets?Why doesn't it include any material and algorithms on strings (regular expression matching, for example)?

But the worst feature of this book is that it doesn't provide bibliographies for the topic it covers.If you need more detailed treatment of matrices or graphs, where should you go for a fuller treatment?It doesn't help at all. ... Read more

Book Description
Mathematical models in the social sciences have become increasingly sophisticated and widespread in the last decade.This period has also seen many critiques, most lamenting the sacrifices incurred in pursuit of mathematical perfection. If, as critics argue, our ability to understand the world has not improved during the mathematization of the social sciences, we might want to adopt a different paradigm.This book examines the three main fields of mathematical modeling--game theory, statistics, and computational methods--and proposes a new framework for modeling. ... Read more

Customer Reviews (1)

Review From Public Choice
Imagine that you're out for a relaxing dinner at your neighborhood bistro. Your waiter, a lanky young lad named Trey, sidles up to your table and describes the evening's specials beginning with a free range, grilled, Sonoma chicken bathed in a white wine and balsamic reduction and peppered with bits of black truffle.You think that the dish sounds wonderful and accept it for consumption with no revisions.

After reading this book, I can only guess that Scott de Marchi's reaction would be a little different.He'd point out that grilling was just one of many options. Alternatively, the chef could have fried it, baked it, braised it, seared it, roasted it, or even cooked it at low temperature in a Ziplock bag.Why did the chef choose grilling? And, oh by the way, why free range Sonoma chicken?Why not organic chicken - wouldn't its stronger taste hold up better to the wine and balsamic reduction? Heck, why not go all out and get one of those chickens that was hand-fed corn mash by Italian monks who gave it daily massages and hour long walks thru Tuscan valleys?And what about the number of possible spice and sauce combinations?Why black truffles?

The chicken entrée suffers from a curse of dimensionality.By modifying our choices on each dimension, we can create enough chicken variations to awe even the late Carl Sagan. Gourmands benefit from the curse - we can expect an original special every week.Social scientists - historians, theorists and empiricists alike - take the curse on the chin.It obliges a rethinking of how we construct and evaluate a model, or so says Scott de Marchi in this fascinating and challenging new book.To feel the effects of the curse, suppose that you're writing an empirical model of why countries go to war. In choosing variables for your regression, you pick ten from a set of twenty.You then toss in an interaction term, chosen with great care from the forty-five possible pairs.You then choose a model specification - linear, log linear, non parametric, or whatever.When you step back and look at the process of creating your empirical model, you realize that you have as many possible regressions as the bistro's chef has chicken entrees.

Suppose instead that you're writing a game theory model of first strikes.Professor de Marchi has a few questions to ask:Is the game one shot or repeated?Are moves sequential or simultaneous?Is information asymmetric?Are the players risk averse?Are preferences separable?

Given the billions of possible model specifications, the task of finding significant coefficients or proving (wink-wink) a general theorem suddenly doesn't look so impressive.Even combining the two: integrating a theoretical model and empirical analysis (EITM anyone?) looks about as hard as cooking up a little Bonferroni chicken to go with that Oregon Coast Pinot Noir. Once aware of the existence of the curse, we can see no shortage of naked emperors (some of whom de Marchi reveals with some relish).We can also try to get around it, to conjure up a counter hex.

The counter hex proposed by de Marchi consists of three parts. First, he wants us to split our data into training sets and testing sets -- a good idea, but it comes with a cost.A little math shows that dividing the data results in data sets that are, on average, only half as big, so we'll need a lot more wars for IR to have any hope of finding statistical significance.

Second, he wants us to analyze classes of models and not individual models with idiosyncratic (and possibly brittle) assumptions.In demanding that we consider classes of models, de Marchi implicitly charges some mathematical theorists with selling magic beans in the form of theorems that rely on specific functional forms.Results for a single functional form do not a general theorem make.The difference between a three person, three alternative example of a Condercet Cycle and Arrow's Possibility Theorem is the difference between predicting that a falling apple will hit the ground and formulating the theory of gravity.But proving general results is not easy.In fact, few general results exist.So why not be honest about the lack of generality rather than cooking up specific models that give the desired result?

The proposed solution, to create a feature space (dimensions on which we make various assumptions) and explore all of the models within that space, sounds good but it creates a problem unless we can increase the birth rates in Pasadena and Rochester. We still have too many models to explore. To get around the problem of too many models and too little time, de Marchi has a novel solution: use computational methods to explore the space of possible models. If we're using specific functional forms anyway, we might as well simulate them and not bother with formal proofs.Simulation is quicker.By simulating within feature space, we can distinguish robust findings from brittle examples.This approach requires combining art and science.We must constrain the feature space so that we're merely stunned and not cursed by the dimensionality.

Third, de Marchi wants our models to be more realistic.(Who doesn't?) But, how do we achieve realism and yet maintain a limited feature space that we explore in depth?Can we be realistic and remain within or at least comfortably near Chris Achen's three-variable world?de Marchi believes that we can, provided that we start simple and build up toward realism.Thus, we have complicated models as the sum of lots of simple models, all of which we understand fully as a result of exploring their feature spaces.As an example of a realistic model, he goes outside of social science and looks at machine chess programs.These programs don't just apply to chess in some metaphorical sense, as in "the Colonel Blotto game captures the essence of chess."They actually play chess and play better than people do.Having a model that plays chess produces a further advantage: the modeler can use real data from games.

Let's suppose we take a vote between continuing with the status quo and accepting de Marchi's vision of the future. The status quo consists of unrealistic, narrow models that we test using all of our data with substantial freedom over what control variables we include. de Marchi's alternative consists of cumulative realistic models (as well as nearby models to make sure that our theory is robust) that are calibrated on training sets and tested on separate data. The vote would be Roosevelt-Landon 1936 all over again.Apart from some holdouts in Vermont and Maine, de Marchi would win everywhere.However, in this election, we don't just pull levers.We have to vote with our heads, which can be thick and slow to respond.

The path de Marchi would like us to take requires nontrivial changes in how we build models and how we test them. Sure we can learn to split our data sets in two.But will we learn Perl?Will we take the time to construct a feature space?And what if that feature space reveals brittleness? Will we bail out and write a paper with quasi-linear preferences or with a one-dimensional preference space?Not only does he require that we learn new tools, he's asking us to change our standards. Rather than bestow awards on books that consist of (a) a captivating anecdote from history (b) a narrow specific functional form model that provides the key intuition (c) an empirical test with ten control variables and one interactive term that demonstrates validity of key intuition and (d) a rich case study that fills in all the gaps, we might see these books as cursed by problems of dimensionality. With so much history, so many models, and so many variables to choose from, these books should be as easy to make as the Chicken Marbella from The Silver Palate.

This critique of the status quo may get under the skin of some readers. Sure, your average PhD student can choose from among thousands of theoretical models and econometric specifications, but finding two that align -- where the econometrics support what the theory predicts - is not as easy as he makes it sound. If it were, we'd have many more papers that met this standard, and we wouldn't have summer courses sponsored by the NSF teaching students how to integrate these methods taught by (among others) Scott de Marchi.Furthermore, the models in these award-winning books aren't all that brittle.They do meet qualitative robustness criteria. Most theorists and econometricians can sniff out rigged models.We can tell a universal insight from a unicorn. When we see a model with quasi-linear preferences or the monotone likelihood ratio property, we know the rabbit has been placed in the hat, and we take the author to task accordingly.

Given that we're all aware of the curse, and we're qualitatively mindful of it when evaluating research, de Marchi's claims seem less provocative, and at the same time, more reasonable.He's advocating that we supplement our reasoned judgment with a scientific approach based on feature spaces and computational models.Any time we can replace subjective criteria with more objective, scientific criteria, we move science forward, which this book urges us to do.

Some critiques may complain that this book explains how to do it, but it doesn't actually do it. True, the book would be stronger if it took us on a complete tour of the shiny new city on the hill that it constructs. A short chapter on how de Marchi built a model (and critics may say an unrealistic one - ouch!) along with a smidgen of Perl code won't sway the masses. The book would be more convincing if had a six hundred page companion volume that took on a puzzle, defined and explored a feature space, tested the robust conclusions out of sample, and then built on the model cumulatively to create a skin tight fit to data.It doesn't.That's because neither he, nor anyone else for that matter, quite knows how to do what he'd like us to do. We're not there, at least not yet, though I suspect that midnight oil burns in Durham.

At present, we have is this slim volume, full of good ideas, deep insights, and practical advice for how to do sound methodological work and how not to do it.Given these strengths, this book should earn a cultish following among graduate students who possess a methodological bent. The book should also become standard reading in introductory methodology courses, at least in those that aren't spineless. (I mean those that don't rely on books.)If it doesn't, the discipline misses an opportunity to improve methodological training.

This is the rare book that suffers from brevity - the examples could be worked out in greater detail. It's not perfect.The argument is flawed in places.For example, not all theory has to be immediately tested to be useful.All of that preference theory comes in handy when he writes his separable preference model, yet little of it was written to be testable.Yes, the light it shines is harsh in places, but overall, the book must be seen as a huge success.It's both deep and approachable.de Marchi manages not to dull our senses with an abundance of jargon or notation.To the contrary, the reader wants even more detail, especially about computational models.

Computational modelers like de Marchi want space at the conferences and in the journals.They want to be taken as seriously as the mathematical modelers. Surely, de Marchi has advanced that cause. As his book makes clear, the existence of computational models provides an opportunity to improve social science. Computational modelers have a powerful technology and they're building a science. They've gained footholds in physics, chemistry, biology, and (egad) mathematics.Social scientists would do far better to be accepting than to be skeptical.

In sum, de Marchi's provocative, incisive, and at times brilliant introduction to computational methods can be thought analogous to one of those early maps of North America.We might quibble with the depiction of the coastline, but we cannot deny the existence of the continent.




... Read more

Book Description

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.

Book Description

Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.

This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.

Customer Reviews (4)

Annie Wu -- Book #2
I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this book.

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.

A Landmark Publication
Josh Epstein's new Opus is a landmark publication in the emerging field of multiagent-based simulation of dynamic social systems. Since Josh is not only one of this still nascent (though burgeoning) field's ablest and most creative practitioners, but also among its most thoughtful critics, the reader of has two treats in store: (1) a generous, and wide-ranging, sampling of case studies (including social networks and evolution, population growth, emergence of economic classes, civil unrest, timing of retirement, the dynamics of adaptive organizations and the spread of infectious disease), and (2) a cogent "meta" discussion of what multiagent models ARE, ARE NOT and how (when their properties and limitations are *not* properly taken account of) they can easily be MISAPPLIED.

Far from suggesting that multiagent-based models are a panacea solution to all (or most) social dynamical systems, Josh's book carefully articulates the conditions for which such an approach IS (and is NOT) appropriate; an approach rarely taken by other, similar, overviews of the field. Indeed, the cogent philosophical discussion in Chapter One - alone! - in which the generativist's position is defined and put into a broader modeling/simulation context, is worth the price of admission; I have not seen a better "manifesto" of multiagent-based modeling elsewhere.

Finally, without taking away any of the inherent "beauty" (in the technical sense) of the often exaggerated concept of "emergence," Josh succeeds admirably in both defining the term, and de-mystifying it, stripping it of some of its unnecessary "quasi-mystical" baggage (at least as it is often portrayed in lay publications).

Anyone who is interested in understanding how agent models may be used to help explore the dynamics of social dynamical systems, should have this book firmly on top of their "must read" list! Josh has generously provided future generations of agent explorers their go-to source of both inspiration and ideas. Well done Josh! ... Read more

Product Description
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 (1)

Pure love
Dr Strangs book, is simply a work of love. If you have no idea of computational engineering, yet you want to see a man who had spent a lifetime in love, and still burried deep within it, experience Dr Strang's words. Pure and perfect. His style unparalleled, his passion unbounded. It needs to be there on every engineers desk, regardless of his discipline. ... Read more

Product Description
This book offers a fundamental and practical introduction to the use of computational methods, particularly finite element methods, in the simulation of fluid flows in porous media. It is the first book to cover a wide variety of flows, including single-phase, two-phase, black oil, volatile, compositional, nonisothermal, and chemical compositional flows in both ordinary porous and fractured porous media. In addition, a range of computational methods are used, and benchmark problems of nine comparative solution projects organized by the Society of Petroleum Engineers are presented for the first time in book form.Computational Methods for Multiphase Flows in Porous Media reviews multiphase flow equations and computational methods to introduce basic terminologies and notation. A thorough discussion of practical aspects of the subject is presented in a consistent manner, and the level of treatment is rigorous without being unnecessarily abstract. Each chapter ends with bibliographic information and exercises.This book can be used as a textbook for graduate or advanced undergraduate students in geology, petroleum engineering, and applied mathematics, and as a reference book for professionals in these fields, as well as scientists working in the area of petroleum reservoir simulation. It can also be used as a handbook for employees in the oil industry who need a basic understanding of modeling and computational method concepts and by researchers in hydrology, environmental remediation, and some areas of biological tissue modeling. Calculus, physics, and some acquaintance with partial differential equations and simple matrix algebra are necessary prerequisites.List of Figures; List of Tables; Preface; Chapter 1: Introduction; Chapter 2: Flow and Transport Equations; Chapter 3: Rock and Fluid Properties; Chapter 4: Numerical Methods; Chapter 5: Solution of Linear Systems; Chapter 6: Single Phase Flow; Chapter 7: Two-Phase Flow; Chapter 8: The Black Oil Model; Chapter 9: The Compositional Model; Chapter 10: Nonisothermal Flow; Chapter 11: Chemical Flooding; Chapter 12: Flows in Fractured Porous Media; Chapter 13: Welling Modeling; Chapter 14: Special Topics; Chapter 15: Nomenclature; Chapter 16: Units; Bibliography; Index. ... Read more

Book Description
This book surveys the major topics that are essential to high-performance simulation on parallel computers or computational clusters. These topics, including programming models, load balancing, mesh generation, efficient numerical solvers, and scientific software, are vital ingredients in the research fields of computer science, numerical analysis, and scientific computing. In addition to presenting the technological basis, this volume addresses selected applications that combine different techniques in order to meet demanding computational challenges. Through contributions from a wide range of internationally acknowledged experts, this book gives a to-the-point and self-containing overview of efficient ways to deal with large-scale simulation problems. ... Read more

Book Description
This book covers modern approaches to Verification and Validation of computer codes, and other topics related to Quantification of Uncertainty and code Quality Assurance. Especially noteworthy are the Method of Manufactured Solutions, a general method for obtaining exact solutions for use in code Verifications, and the Grid Convergene Index, a method for uniform reporting of grid convergence tests. The methods and issues are applicable to commercial codes as well as to in-house developed research codes, and to all problems modeled by partial differential equations; examples include Computational Fluid Dynamics, groundwater flow and transport modeling, electrodynamics, etc. The book provides useful formulas and practical guidance for error estimation and for experimental Validation. ... Read more

Book Description
Students embarking on their studies in chemical, mechanical, aerospace, energy, and environmental engineering will face continually changing combustion problems, such as pollution control and energy efficiency, throughout their careers. Approaching these challenges requires a deep familiarity with the fundamental theory, mathematics, and physical concepts of combustion. Based on more than two decades of teaching experience, Combustion Science and Engineering lays the necessary groundwork while using an illustrative, hands-on approach.

Taking a down-to-earth perspective, the book avoids heavy mathematics in the first seven chapters and in Chapter 17 (pollutants formation and destruction), but considers molecular concepts and delves into engineering details. It begins with an outline of thermodynamics; basics of thermochemistry and chemical equilibrium; descriptions of solid, liquid, and gaseous fuels; chemical kinetics and mass transfer; and applications of theory to practical systems. Beginning in chapter 8, the authors provide a detailed treatment of differential forms of conservation equations; analyses of fuel combustion including jet combustion and boundary layer problems; ignition; flame propagation; interactive and group combustion; pollutant formation and control; and turbulent combustion.

In addition, this textbook includes abundant examples, illustrations, and exercises, as well as spreadsheet software in combustion available for download. This software allows students to work out the examples found in the text. Combustion Science and Engineering imparts the skills and foundational knowledge necessary for students to successfully approach and solve new problems. ... Read more

Book Description

The numerical treatment of partial differential equations with particle methods and meshfree discretization techniques is a very active research field both in the mathematics and engineering community. Due to their independence of a mesh, particle schemes and meshfree methods can deal with large geometric changes of the domain more easily than classical discretization techniques. Furthermore, meshfree methods offer a promising approach for the coupling of particle models to continuous models. This volume of LNCSE is a collection of the papers from the proceedings of the Second International Workshop on Meshfree Methods held in September 2003 in Bonn. The articles address the different meshfree methods (SPH, PUM, GFEM, EFGM, RKPM, etc.) and their application in applied mathematics, physics and engineering. The volume is intended to foster this new and exciting area of interdisciplinary research and to present recent advances and results in this field.

Product Description
This applications-oriented book teaches students and engineers how to employ mathematical techniques for simulation and data processing using Mathcad. It is designed as a reference for practicing engineers or for use as a standard university course text, illustrating practical examples from computational science (such as optics, environmental science, chemistry, biology, tomography, economics, etc). The authors describe the most popular classical algorithms and modern techniques with all of the examples computed in Mathcad (on CD-ROM). Brief Table of Contents PART 1. EXPERIMENT PROCESSINGChapter 1. Data Processing Chapter 2. Inverse Problems PART 2. MODELLING Chapter 3. Ordinary Differential Equations: Cauchy Problems Chapter 4.Nonlinear Dynamics Chapter 5.Boundary-Value Problems Chapter 6. Partial Differential Equations Appendix A. References Appendix B. CD-ROM. Index ... Read more

Book Description
From the reviews: "This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry. ... ... The book is well organized and lucidly written; a timely contribution by two founders of the field. It clearly demonstrates that computational geometry in the plane is now a fairly well-understood branch of computer science and mathematics. It also points the way to the solution of the more challenging problems in dimensions higher than two." #Mathematical Reviews#1 "... This remarkable book is a comprehensive and systematic study on research results obtained especially in the last ten years. The very clear presentation concentrates on basic ideas, fundamental combinatorial structures, and crucial algorithmic techniques. The plenty of results is clever organized following these guidelines and within the framework of some detailed case studies. A large number of figures and examples also aid the understanding of the material. Therefore, it can be highly recommended as an early graduate text but it should prove also to be essential to researchers and professionals in applied fields of computer-aided design, computer graphics, and robotics." #Biometrical Journal#2 ... Read more

Customer Reviews (6)

Christians fundimentalists have the King James Version, Computational geometrists have...
This book is to computational geometrists what the King James Version of the Bible is to christian fundimenalists.Even though newer translations of the Bible are easier to read, somehow nothing sounds quite so authentically like the voice of God than those Elisibethen cadences, written in an almost archaic language....

...similarly for this book.Many times, the descriptions of algorithms presented in this book are made unnecesarily hard by very arcane langauge.

But this book is authoritative and definitive in a way that no other text on computational geometry is ever likely to achieve.Even though there are any number of books which are newer and easier to read, it seems like this the one book on the shelf of every serious computational geometer I know.

This book is history
This book is a classic, in fact the author's PhD thesis created this field, but this book is too old for any meaningful graduate work.There are new bounds and algorithms on almost all topics, which makes this a somewhat undesirable book.Also, this book has failed to keep me interested in it, while I am reading it...

Very useful for code development. Very clear and readable.
The ideas and algorithms presented in this book are clear enough for straight implementation in code. I have long experience in developing comercial and production software for VLSI layout applications, which made extensive use of the algorithms presented in this book.
I also use some chapters of this book as a part of a graduate course in VLSI layout algorithms being tought at the Technion, Israel. The contents of this book is well understood by EE and CS students.
I personally love this book, which introduced me into the area of computational geometry and its applications.

Useful but thick
Most of the papers that I've read on computational geometry refer to this text -- and for good reason. There's many good algorithms to be found here.

The book only gets 4 stars because it's hard to read. It took me several tries to pick up the ideas in this text. I think the De Berg text is MUCH easier to read.

The book is also getting a little dated. Some of the topics have come a long way since the 80's.

This book seems to be in most University libraries if you have that option.

Still interesting after so many years ...
I have just happened to exhume this book from my library, after it spent some years gathering dust above the shelf. In spite of the long time I have not being reading it, it still retains the full meaning it showed me when I was using in calculations relating radar domain definition. May be the textbook wins by far the comparison to the current vague and inflated computer publications, may be it is not a manager-oriented issue but it is for nearly specialistic use, you find in it clearly stated, and straight, answers to the questions you meet, or at least a definite reference where a more detailed explanation can be find. It presents interesting problems, and explains you how to solve them. I think it is the best you can say about a computer science book. ... Read more

Book Description
Computer science and physics have been closely linked since the birth of modern computing.In recent years, an interdisciplinary area has blossomed at the junction of these fields, connecting insights from statistical physics with basic computational challenges.Researchers have successfully applied techniques from the study of phase transitions to analyze NP-complete problems such as satisfiability and graph coloring.This is leading to a new understanding of the structure of these problems, and of how algorithms perform on them.Computational Complexity and Statistical Physics will serve as a standard reference and pedagogical aid to statistical physics methods in computer science, with a particular focus on phase transitions in combinatorial problems.Addressed to a broad range of readers, the book includes substantial background material along with current research by leading computer scientists, mathematicians, and physicists.It will prepare students and researchers from all of these fields to contribute to this exciting area. ... Read more

Book Description
By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics as the nature of concepts, hypothesis formation, analogy, and theory justification.

Paul Thagard is Professor of Philosophy at the University of Waterloo. ... Read more

Customer Reviews (1)

A philosopher of science for the XXI century
If, as I believe, persons enjoy what they do well, then Dr. Paul Thagard must enjoy computational philosophy of science very much.Computational philosophy of science is the new frontier in philosophy of science today, and it portends to become essential to and definitive of twenty-first century philosophy of science.

Thagard is one of the few academic philosophers of science who has demonstrated the requisite technical skills in computer systems analysis to make contributions to this new area.I believe that all of his works are worth reading, and this Introduction should be requisite reading for the graduate student in philosophy of science preparing for the twenty-first century.Five stars are not enough!

Readers interested in my commentary on Thagard are to view my online book titled History of Twentieth-Century Philosophy of Science at my com web site philsci with free downloads.

Thomas J. Hickey ... Read more

Book Description
Computational methods have become the dominant technique in many areas of science. This book contains the first systematic philosophical account of these new methods and their consequences for scientific method. This book will be of interest to philosophers of science and to anyone interested in the role played by computers in modern science. ... Read more

Customer Reviews (1)

Excellent and stimulating
I can recommend this book to anyone interested in how computers are changing science. Although the book contains some technical material addressed to academic philosophers, most of the material is accessible to anyone interested in general issues of scientific method and overall the prose is exceptionally well written. There is one chapter on empiricism that ties together the use of instruments in science and the specifically computational material later in the book. Definitely recommended for anyone in computer science and scientific fields that use computers since this is the first book by a philosopher to discuss these new methods. ... Read more

Book Description

This book is about computing eigenvalues, eigenvectors and invariant subspaces of matrices. The treatment includes generalized and structured eigenvalue problems, such as Hamiltonian or product eigenvalue problems. All vital aspects of eigenvalue computations are covered: theory, perturbation analysis, algorithms, high performance methodologies and software. The reader will learn about recently developed techniques which substantially improve the performance of some of the most widely numerical methods, the QR and the QZ algorithm as well as Krylov subspace methods. A unique feature of this book is the detailed treatment of structured eigenvalue problems, providing insight on accuracy and efficiency gains to be expected from algorithms that take the structure of a matrix into account.