Simulation Books

The mathematical theory of the micropolar fluids is given. This book is write in a good style. The author is a expert in these questions.

I give it 4 stars for being one of the only books on the topic of microsimulation/agent-based modeling in finance.

The author's research is very interesting and promising. The book reviews similar microsimulation attempts by others.

However, there is no guidance as to the implementation of microsimulation studies in finance. The eauations/models of finance are easily found elsewhere .... but how do you turn them into a simulation project (?)...

This combines geat pictures with very planned out sections that make this book so easy to use. It will help veterans and new. It will be read constantly by me to make me the best.

Dr. Franks (Dupont, retired) wrote his classic to demonstrate a systematic method of developing a (near) computer platform independent mathematical modeling library of routines to solve unsteady state chemical engineering problems. The book focuses initially on the types of models that can be readily solved, and the thought process used to create such models. Emphasis is placed on the use of engineering first principles to build deterministic systems.

Routines for solving systems of algebraic and differential equations are first developed, with others to display a time-based 'table' of the simulation results. The second half of the book develops his DYFLO routines for single, staged or distributed unit operations, with many diagrams and examples. In the later chapters, partial differential equations and process control models are developed.

Dr. Franks has been referred to as the "Father of Simulation", and after building some of his many examples, I believe that the phrase is quite appropriate. I first bought this book in 1973, but did the most work with it after the availability of personal computers. It was amazing. The simulations changed the way I thought about unit operations, engineering problem solving, and process control simulations. This thought process shaped the modeling framework that I was developing (building, and tuning interactive process control simulations).

The source code in Modeling and Simulation is provided in FORTRAN IV, which was the current engineering language 'standard'. It is quite compact, again to run on the machines of that time. I found that it was relatively straight-forward (albeit that you will need some other reference books) to convert to other languages in order to use the interpreters/compilers of choice.

I rate this book 4 stars instead of 5 because : 1) it's too expensive and, 2) the FORTRAN IV code and it's structure is dated. The programs needs to be commented and rewritten both more linearly and in a current language such as DELPHI, VB or C++ Builder (or Visual C++). You can sort it out if you are an experienced programmer, but the programs need to be restructured. This book desperately needs to provide at least a CD with the source code.

A company called QMC appears to offer a Windows 95/98 simulation framework for many of Dr. Frank's examples, and many other models and simulations.

The study of the physics of the brain from the standpoint of dynamical systems was very popular during the 1980's. The theory of chaotic dynamical systems, and the accompanying concepts of strange attractors, horseshoe maps, and fractal basins of attraction was the subject of intense research at that time. It was inevitable perhaps that these theories would be applied to the understanding of the brain, given the dynamical nature of the neuronal synapse. This book, published in 1989, gives a good overview of what was known at the time. It could be read by anyone with a background in dynamical systems and some elementary knowledge of brain biology. The mathematics is also straightforward in that the author does not bring in any of the heavy tools from differential topology or measure theory, which is normally done in discussions of dynamical systems.

There are some points made in the book that must be understood by the reader because the author feels that they are needed to build a successful model of the brain. For example, he discusses the notion of an `input system', which is a system that, for each input, produces and output with the same "status." Cognitive discrimination must be used at the input level, if one is to avoid the use of the `homunculus' (the little external observer), for distinguishing between "good" and "bad" outputs. The major task in the author's view is to produce "exceptional" input-output relations, i.e. relations that correspond to intuitions about cognitive processes. A successful brain model, i.e. one that is able to incorporate memory, should be able to distinguish between stimuli that are familiar from those that are to be submitted to the brain for processing or learning. Thus the model must avoid the use of what the author calls `spontaneous computations', which require an external observer (the homunculus again) to interpret the relation between the input and the output. The author gives an example of a system that performs only spontaneous computations early on in the book. Hence the author proposes the use of artificial neural networks (ANNs) to avoid the occurrence of spontaneous computations. An ANN organizes stimuli in association classes represented by an attractor, and all the stimuli in a particular class are associated with the attractor to which they flow. The author feels that ANNs are more adept at respecting the requirement that for mental computations, which are essentially operations on temporal sequences of data, some record of the initial input sequence must be carried along on a parallel channel, in order to provide the outcome with specific "meaning" and a correspondence to the assigned task.

These considerations on the dependence of the processing on the initial input motivate the author to discuss the role of ergodicity in the dynamics of the neural systems of the brain. As the author shows, any generic system subjected to noise will be ergodic, so that eventually the system will access each of its possible states in a manner that is completely independent of the initial state. The author points out two ways in which ergodicity can be avoided: one is to assume that the network is noiseless, and thus only certain moves are allowed from each vertex; the other is to assume that `cooperative phenomena' is present. Since the first possibility is rather exceptional, the author chooses the second, and gives detailed discussion on how cooperative behavior can arise in ANNs. One interesting, and ubiquitous example that he discusses for cooperativity as an emergent property is the Ising model. Mathematically, the breaking of ergodicity involves the taking of the thermodynamic limit, and a necessary condition for emergence is this context is the asymptotic degeneracy of the eigenvalues. To illustrate how this is done, the author uses the solution of a master equation that characterizes the probabilities of making transitions from one state to another in the system.

In order to build a credible model of the neuronal processes of the brain, the author is aware that such a model has to be able to deal with input in the form of temporal sequences, and not just single patterns. He devotes an entire chapter to this in the book, motivating his discussion with the notion of a `central pattern generator' (CPG). The simplicity of CPGs is a concern and the author is aware that such simplicity does not exist in models of cognitive processes. Nevertheless the modeling of CPGs using neural networks can add credence to the program to model general brain processes in terms of neural networks, complex as they can be.

One of course must be able to deal with both the storage and the retrieval of temporal sequences. After discussing some of the early research dealing with these needs, the author then reviews a strategy for dealing with temporal sequences that involves the notion of a `quasi-attractor', which is a network state that acts like an attractor for a short period of time. Quasi-attractors are used to delay the transfer of information out of the attractor. Thus the transitions are governed by synapses that have a time delay. The influence of a pre-synaptic neuron through these synapses will arrive later than the influence coming through a `stabilizing' synapse. The latter type of synapse arises because of the `stabilizing' term in the network model that ensures that if the network is in a state that is identical to a stored pattern then the network will remain there. The author shows how the network can use these delayed transitions to deal with temporal sequences in a manner that is acceptable, i.e. in a way that the `cognition time' is of the order of magnitude of the delay. The author discusses an example dealing with the counting of chimes, in order to give credence to his constructions. In this example it is seen that the network resides in each of the quasi-attractors for a long enough time so as to allow the output neurons to identify the cognitive event.

"Modeling for Learning Organizations" builds off of the extensive experience of top professors and consultants using the SD tools to test strategies and build an understanding of firm and industry dynamics.

"Modeling" also includes a section overviewing the various simulation software packages available to modelers. Though developers like High-Performance Systems, Vensim, Pugh-Roberts, and PowerSim have made product enhancements to date, the sections from each company provide a great introduction to what is out there how each package can be applied.

The most valuable aspect of the book is probably in the case studies and methodological explorations of several authors. A number of key insights are offered as authors reflect upon the successes and shortcoming of the methods each chose to use to explore and develop models in a variety of business and public environments.

This is definitely a must have for any SD library.

Application of Petri net in Flexible Manufacturing Systems modelling and perfomance analysis

Modelling for Management by George P. Richardson is the first of two volumes on the subject of systems thinking and dynamic modelling. The first chapter / article provides the reader with insight into the very practice of modelling for management and exposes the fallacies inherent in many of the more common approaches. Subsequent chapters explore a broad spectrum of cases and applications of systems thinking to management. It is an excellent introduction and reference book for all readers interested in the field.

Molecular modeling is a topic every scientist is talking about. At least every physicist and chemist. When I started my diploma work doing molecular simulations of polymers, Gelins book was the first i had to read. and indeed it gives a good overview with very many references to standard literature. The book is diveded into twelve chapters starting with how polymer chains are built, how to consider the detailed interaction mechanisms and then going on to how specific polymeric properties, e.g. mechanical behaviour, scattering, conductivity ... can be and are simulated. The book suits best for people who want to get an introduction to the subject. For those already familiar with the principles of molecular modeling i'd suggest the book of Allen and Tildesley on "Computer Simulation of Liquids".

This is a very good introduction to the layperson(nonmath type)for monte carlo simulation. It is concise and written with clarity. I highly recommend it.