Introduction to Computational Science: Modeling and Simulation for the Sciences

Introduction to Computational Science: Modeling and Simulation for the Sciences Review

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

Digital Signal Integrity: Modeling and Simulation with Interconnects and Packages

Digital Signal Integrity: Modeling and Simulation with Interconnects and Packages Review

For advanced courses in digital design.This state-of-the-art book provides students with techniques for predicting and achieving target performance levels. Gives students all the theory, practice, general signal integrity issues, and leading-edge experimental techniques they need to accurately model and simulate those interconnections and predict real-world performance.

Stochastic Modeling: Analysis and Simulation (Dover Books on Mathematics)

Stochastic Modeling: Analysis and Simulation (Dover Books on Mathematics) Review

A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Each chapter opens with an illustrative case study, and comprehensive presentations include formulation of models, determination of parameters, analysis, and interpretation of results. 1995 edition.

Forecasting and Simulating Software Development Projects: Effective Modeling of Kanban & Scrum Projects using Monte-carlo Simulation

Forecasting and Simulating Software Development Projects: Effective Modeling of Kanban & Scrum Projects using Monte-carlo Simulation Review

Forecasting and Simulating Software Development Projects explains how to effectively model Kanban and Scrum projects to get accurate forecasts of cost, delivery dates and staff requirements.

Modeling using Monte-carlo simulation allows rapid what-if analysis to find options that minimize cost and delivery time, whilst maximizing revenue. Simulation lets you hit target delivery dates, and shows the impact of hiring (or losing) staff with certain skillsets, taking software project leadership to a new level of maturity.

Target audience and key takeaways -

Project Managers: Understand modeling and forecast projects, and how to simulate those models to answer questions regarding delivery dates, cost, and staffing needs.

Development Managers and Team Leads: Understand how to reduce the amount of estimation required for cost and date forecasts, and determining what development events cause the most impact.

Executive Leadership: Understand how multiple teams can co-ordinate their forecasts in a methodical way, and provide a consistent approach to risk management and decision making.

Venture Capital Investors: Understand how to obtain reliable cost and date forecasts for potential investments and how to compare different software project investment portfolios.

Topics include -

Simulating Scrum and Kanban project methodologies

Forecasting the probability of hitting delivery date & costs

Hiring the right team size and skill mix

Creating visual animations and videos to sell solutions to others

Finding what model inputs are critical to delivery date

Effective (and minimal) story estimation and grouping strategies

Capturing the project deliverables and story backlog

Modeling development events: defects, added scope and blocking events

Reverse engineering real-world data to improve model accuracy

Simulation Modeling and Analysis

Simulation Modeling and Analysis Review

For courses in simulation offered at the advanced undergraduate or graduate level in departments of industrial engineering or schools of business, this text provides a state-of-the-art treatment of all of the important aspects of a simulation study, including modelling, simulation software, validation, selecting input probability distributions, and output data analysis. The new edition includes the most up-to-date research developments and many more examples and problems.

Discrete-Event Simulation: Modeling, Programming, and Analysis (Springer Series in Operations Research and Financial Engineering)

Discrete-Event Simulation: Modeling, Programming, and Analysis (Springer Series in Operations Research and Financial Engineering) Review

"This is an excellent and well-written text on discrete event simulation with a focus on applications in Operations Research. There is substantial attention to programming, output analysis, pseudo-random number generation and modelling and these sections are quite thorough. Methods are provided for generating pseudo-random numbers (including combining such streams) and for generating random numbers from most standard statistical distributions." --ISI Short Book Reviews, 22:2, August 2002

Applied Groundwater Modeling: Simulation of Flow and Advective Transport

Applied Groundwater Modeling: Simulation of Flow and Advective Transport Review

Creating numerical groundwater models of field problems requires careful attention to describing the problem domain, selecting boundary conditions, assigning model parameters, and calibrating the model. This unique text describes the science and art of applying numerical models of groundwater flow and advective transport of solutes.

Key Features
* Explains how to formulate a conceptual model of a system and how to translate it into a numerical model
* Includes the application of modeling principles with special attention to the finite difference flow codes PLASM and MODFLOW, and the finite-element code AQUIFEM-1
* Covers model calibration, verification, and validation
* Discusses pathline analysis for tracking contaminants with reference to newly developed particle tracking codes
* Makes extensive use of case studies and problems

Simulation and Optimization in Finance + Website: Modeling with MATLAB, @Risk, or VBA (Frank J. Fabozzi Series)

Simulation and Optimization in Finance + Website: Modeling with MATLAB, @Risk, or VBA (Frank J. Fabozzi Series) Review

An introduction to the theory and practice of financial simulation and optimization

In recent years, there has been a notable increase in the use of simulation and optimization methods in the financial industry. Applications include portfolio allocation, risk management, pricing, and capital budgeting under uncertainty.

This accessible guide provides an introduction to the simulation and optimization techniques most widely used in finance, while at the same time offering background on the financial concepts in these applications. In addition, it clarifies difficult concepts in traditional models of uncertainty in finance, and teaches you how to build models with software. It does this by reviewing current simulation and optimization methodology-along with available software-and proceeds with portfolio risk management, modeling of random processes, pricing of financial derivatives, and real options applications.

• Contains a unique combination of finance theory and rigorous mathematical modeling emphasizing a hands-on approach through implementation with software
• Highlights not only classical applications, but also more recent developments, such as pricing of mortgage-backed securities
• Includes models and code in both spreadsheet-based software (@RISK, Solver, Evolver, VBA) and mathematical modeling software (MATLAB)

Filled with in-depth insights and practical advice, Simulation and Optimization Modeling in Finance offers essential guidance on some of the most important topics in financial management.

Simulation, Fourth Edition (Statistical Modeling and Decision Science)

Simulation, Fourth Edition (Statistical Modeling and Decision Science) Review

Ross's Simulation, Fourth Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes.
This text explains how a computer can be used to generate random numbers, and how to use these random numbers to generate the behavior of a stochastic model over time. It presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.

New to this Edition:
-More focus on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis
-A chapter on Markov chain monte carlo methods with many examples
-Unique material on the alias method for generating discrete random variables

Simulation, Third Edition (Statistical Modeling and Decision Science)

Simulation, Third Edition (Statistical Modeling and Decision Science) Review

Sheldon Ross' Simulation, Third Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes.

This new edition provides a comprehensive, in-depth, and current guide for constructing probability models and simulations for a variety of purposes. It features new information, including the presentation of the Insurance Risk Model, generating a Random Vector, and evaluating an Exotic Option. Also new is coverage of the changing nature of statistical methods due to the advancements in computing technology.

Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4

Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4 Review

Scilab and its Scicos block diagram graphical editor, with a special emphasis on modeling and simulation tools. The first part is a detailed Scilab tutorial, and the second is dedicated to modeling and simulation of dynamical systems in Scicos. The concepts are illustrated through numerous examples, and all code used in the book is available to the reader.

Aircraft Dynamics: From Modeling to Simulation (CourseSmart)

Aircraft Dynamics: From Modeling to Simulation (CourseSmart) Review

Napolitano's Aircraft Dynamics is designed to help readers extrapolate from low level formulas, equations, and details to high level comprehensive views of the main concepts. The text also helps readers with fundamental skills of learning the "basic modeling" of the aircraft aerodynamics and dynamics. The main objective is to organize the topics in "modular blocks" each of them leading to the understanding of the inner mechanisms of the aircraft aerodynamics and dynamics, eventually leading to the development of simple flight simulations schemes.

Pharmacokinetic-Pharmacodynamic Modeling and Simulation

Pharmacokinetic-Pharmacodynamic Modeling and Simulation Review

This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated.