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Modeling And Simulation In Python
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Book Synopsis Modeling and Simulation in Python by : Allen B. Downey
Download or read book Modeling and Simulation in Python written by Allen B. Downey and published by No Starch Press. This book was released on 2023-05-30 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math. Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.
Book Synopsis Introduction to Modeling and Simulation with MATLAB® and Python by : Steven I. Gordon
Download or read book Introduction to Modeling and Simulation with MATLAB® and Python written by Steven I. Gordon and published by CRC Press. This book was released on 2017-07-12 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.
Book Synopsis Hands-On Simulation Modeling with Python by : Giuseppe Ciaburro
Download or read book Hands-On Simulation Modeling with Python written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2020-07-17 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features Learn to create a digital prototype of a real model using hands-on examples Evaluate the performance and output of your prototype using simulation modeling techniques Understand various statistical and physical simulations to improve systems using Python Book Description Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learn Gain an overview of the different types of simulation models Get to grips with the concepts of randomness and data generation process Understand how to work with discrete and continuous distributions Work with Monte Carlo simulations to calculate a definite integral Find out how to simulate random walks using Markov chains Obtain robust estimates of confidence intervals and standard errors of population parameters Discover how to use optimization methods in real-life applications Run efficient simulations to analyze real-world systems Who this book is for Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
Book Synopsis A Student's Guide to Python for Physical Modeling by : Jesse M. Kinder
Download or read book A Student's Guide to Python for Physical Modeling written by Jesse M. Kinder and published by Princeton University Press. This book was released on 2021-08-03 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This guide introduces a wide range of useful tools, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Animation Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.
Book Synopsis Computational Modeling and Visualization of Physical Systems with Python by : Jay Wang
Download or read book Computational Modeling and Visualization of Physical Systems with Python written by Jay Wang and published by John Wiley & Sons. This book was released on 2015-12-21 with total page 986 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Modeling, by Jay Wang introduces computational modeling and visualization of physical systems that are commonly found in physics and related areas. The authors begin with a framework that integrates model building, algorithm development, and data visualization for problem solving via scientific computing. Through carefully selected problems, methods, and projects, the reader is guided to learning and discovery by actively doing rather than just knowing physics.
Book Synopsis Principles of Modeling and Simulation by : John A. Sokolowski
Download or read book Principles of Modeling and Simulation written by John A. Sokolowski and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores wide-ranging applications of modeling and simulation techniques that allow readers to conduct research and ask "What if?" Principles of Modeling and Simulation: A Multidisciplinary Approach is the first book to provide an introduction to modeling and simulation techniques across diverse areas of study. Numerous researchers from the fields of social science, engineering, computer science, and business have collaborated on this work to explore the multifaceted uses of computational modeling while illustrating their applications in common spreadsheets. The book is organized into three succinct parts: Principles of Modeling and Simulation provides a brief history of modeling and simulation, outlines its many functions, and explores the advantages and disadvantages of using models in problem solving. Two major reasons to employ modeling and simulation are illustrated through the study of a specific problem in conjunction with the use of related applications, thus gaining insight into complex concepts. Theoretical Underpinnings examines various modeling techniques and introduces readers to two significant simulation concepts: discrete event simulation and simulation of continuous systems. This section details the two primary methods in which humans interface with simulations, and it also distinguishes the meaning, importance, and significance of verification and validation. Practical Domains delves into specific topics related to transportation, business, medicine, social science, and enterprise decision support. The challenges of modeling and simulation are discussed, along with advanced applied principles of modeling and simulation such as representation techniques, integration into the application infrastructure, and emerging technologies. With its accessible style and wealth of real-world examples, Principles of Modeling and Simulation: A Multidisciplinary Approach is a valuable book for modeling and simulation courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for researchers and practitioners working in statistics, mathematics, engineering, computer science, economics, and the social sciences who would like to further develop their understanding and knowledge of the field.
Book Synopsis Pyomo – Optimization Modeling in Python by : William E. Hart
Download or read book Pyomo – Optimization Modeling in Python written by William E. Hart and published by Springer Science & Business Media. This book was released on 2012-02-15 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.
Book Synopsis Computer Simulation by : Yahya Esmail Osais
Download or read book Computer Simulation written by Yahya Esmail Osais and published by CRC Press. This book was released on 2017-11-28 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer simulation is an effective and popular universal tool that can be applied to almost all disciplines. Requiring only basic knowledge of programming, mathematics, and probability theory, Computer Simulation: A Foundational Approach Using Python takes a hands-on approach to programming to introduce the fundamentals of computer simulation. The main target of the book is computer science and engineering students who are interested mainly in directly applying the techniques to their research problems. The book will be of great interest to senior undergraduate and starting graduate students in the fields of computer science and engineering and industrial engineering.
Book Synopsis Agent-Based Modeling and Simulation I by : Juan Martín García
Download or read book Agent-Based Modeling and Simulation I written by Juan Martín García and published by Juan Martín García. This book was released on 2021-02-08 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Agent Based Model (ABM) allows simulating the actions and interactions of many agents or entities in order to evaluate their impact on the system as a whole. These models are used in areas such as industry, business, biology, ecology, and the social sciences. CONTRIBUTIONS - IMMEDIATE RESULTS. From the first pages the reader is already able to create a model. - FREE SOFTWARE. The use of specific and free software for personal and educational use. - WITHOUT PRIOR TRAINING. Knowing how to program in Java, C ++, Pyton, Anylogic, etc. is not required. - GUIDE. A neat guide that explains each step in detail, for quick learning. - MODELS. The explanation of 40 didactic models, created to learn progressively. - FIGURES. The support of more than 1000 figures to advance clearly in each stage. - VIDEOS. The models described, together with various help videos, can be downloaded. - PRACTICAL. A practical approach allows the reader to see the possible applications to their environment. - EXPERIENCE. The teaching experience of the author and the reviewers has allowed the text to be refined to the maximum. AUTHOR AND REVIEWERS Juan Martín García is a Doctor of Industrial Engineering in Business Organization from the UPC (Spain) and a Diploma from the Sloan School of Management at MIT (USA). He has more than 30 years of experience as a consultant for companies and public organizations using simulation models based on System Dynamics. Professor at several Spanish and Latin American universities, he teaches online courses at Vensim https://vensim.com/vensim-online-courses/ (in English) and System Dynamics at ATC-Innova http://atc-innova. com/ (Spanish). He is the author of books and lectures on business, social and environmental applications of simulation models. - Dr. Francisco Campuzano Bolarín, Professor of Business Organization at the Polytechnic University of Cartagena (UPCT). - Lening Mora, M.S Environmental & Occupational Health (San Diego, California) and Postgraduate Diploma in Healthcare Modeling and Simulation at Naval Postgraduate School (Monterey, California USA). - Professor Gavin Melles, PhD, MSc Swinburne University (Victoria, Australia). INDEX Presentation software Installation Working screen A model in 1 minute Concepts Functions and tables Variables Model: Traffic light Model: Paris Rome Attributes Model: Rio Bravo 2 Model: Truck Fleet Collections and aggregates Model: Dragons and Castles Model: Parents and children Model: The Four Pirates References Model: White and Black Model: White and Black 2 Model: White and Black 3 Comments Tools Entities initial parameters Model: Horse Racing Temporal parameters Model: Satellite Launch External data entities Import initial data Import time series data Model: My three rabbits Exercises Model: Rabbit Population Model: Rabbit Population 2 Model: Rabbit Population 3 Model: Rabbit population 4 Model: Rabbit population 5 Model: Sweet candies Model: Cheese shop Model: Cheese Shop 2 Model: Formula 1 drivers Model: Patients and hospitals Model: Horse breeding Model: Horse breeding 2 Model: Horse breeding 3 Model: Horse breeding 4 Model: Horse breeding 5 Model: Horse breeding 6 Model: Horse breeding 7 Model: Fighter aircraft Model: Fighter Aircraft 2 Model: Fishing in three seas Model: Fishing in three seas 2 Model: Fishing in three seas 3 Model: Fishing in three seas 4 Model: Fishing in three seas 5 Model: Fishing in three seas 6 Model: Gold Market Model: Gold Market 2 Model: Gold Market 3 Model: Gold Market 4 Model: Eco Restaurant Model: Beer Game
Book Synopsis Practical Time Series Analysis by : Aileen Nielsen
Download or read book Practical Time Series Analysis written by Aileen Nielsen and published by O'Reilly Media. This book was released on 2019-09-20 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
Book Synopsis Derivatives Analytics with Python by : Yves Hilpisch
Download or read book Derivatives Analytics with Python written by Yves Hilpisch and published by John Wiley & Sons. This book was released on 2015-08-03 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.
Download or read book Think Complexity written by Allen Downey and published by "O'Reilly Media, Inc.". This book was released on 2012-03-02 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.
Book Synopsis Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica by : Peter Fritzson
Download or read book Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica written by Peter Fritzson and published by John Wiley & Sons. This book was released on 2011-10-03 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master modeling and simulation using Modelica, the new powerful, highly versatile object-based modeling language Modelica, the new object-based software/hardware modeling language that is quickly gaining popularity around the world, offers an almost universal approach to high-level computational modeling and simulation. It handles a broad range of application domains, for example mechanics, electrical systems, control, and thermodynamics, and facilitates general notation as well as powerful abstractions and efficient implementations. Using the versatile Modelica language and its associated technology, this text presents an object-oriented, component-based approach that makes it possible for readers to quickly master the basics of computer-supported equation-based object-oriented (EOO) mathematical modeling and simulation. Throughout the text, Modelica is used to illustrate the various aspects of modeling and simulation. At the same time, a number of key concepts underlying the Modelica language are explained with the use of modeling and simulation examples. This book: Examines basic concepts such as systems, models, and simulations Guides readers through the Modelica language with the aid of several step-by-step examples Introduces the Modelica class concept and its use in graphical and textual modeling Explores modeling methodology for continuous, discrete, and hybrid systems Presents an overview of the Modelica Standard Library and key Modelica model libraries Readers will find plenty of examples of models that simulate distinct application domains as well as examples that combine several domains. All the examples and exercises in the text are available via DrModelica. This electronic self-teaching program, freely available on the text's companion website, guides readers from simple, introductory examples and exercises to more advanced ones. Written by the Director of the Open Source Modelica Consortium, Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica is recommended for engineers and students interested in computer-aided design, modeling, simulation, and analysis of technical and natural systems. By building on basic concepts, the text is ideal for students who want to learn modeling, simulation, and object orientation.
Book Synopsis Introduction to Computational Models with Python by : Jose M. Garrido
Download or read book Introduction to Computational Models with Python written by Jose M. Garrido and published by CRC Press. This book was released on 2015-08-28 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy m
Book Synopsis Inventory Optimization by : Nicolas Vandeput
Download or read book Inventory Optimization written by Nicolas Vandeput and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-08-24 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg
Book Synopsis Financial Modelling in Python by : Shayne Fletcher
Download or read book Financial Modelling in Python written by Shayne Fletcher and published by John Wiley & Sons. This book was released on 2010-10-28 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
Book Synopsis A Practical Introduction to the Simulation of Molecular Systems by : Martin J. Field
Download or read book A Practical Introduction to the Simulation of Molecular Systems written by Martin J. Field and published by Cambridge University Press. This book was released on 2007-07-19 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Molecular simulation is a powerful tool in materials science, physics, chemistry and biomolecular fields. This updated edition provides a pragmatic introduction to a wide range of techniques for the simulation of molecular systems at the atomic level. The first part concentrates on methods for calculating the potential energy of a molecular system, with new chapters on quantum chemical, molecular mechanical and hybrid potential techniques. The second part describes methods examining conformational, dynamical and thermodynamical properties of systems, covering techniques including geometry-optimization, normal-mode analysis, molecular dynamics, and Monte Carlo simulation. Using Python, the second edition includes numerous examples and program modules for each simulation technique, allowing the reader to perform the calculations and appreciate the inherent difficulties involved in each. This is a valuable resource for researchers and graduate students wanting to know how to use atomic-scale molecular simulations. Supplementary material, including the program library and technical information, available through www.cambridge.org/9780521852524.
