Search Results for "modeling-with-data-tools-and-techniques-for-scientific-computing"

Modeling with Data

Modeling with Data

Tools and Techniques for Scientific Computing

  • Author: Ben Klemens
  • Publisher: Princeton University Press
  • ISBN: 9781400828746
  • Category: Mathematics
  • Page: 472
  • View: 4738
DOWNLOAD NOW »
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Data Analysis with Open Source Tools

Data Analysis with Open Source Tools

A Hands-On Guide for Programmers and Data Scientists

  • Author: Philipp K. Janert
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 9781449396657
  • Category: Computers
  • Page: 540
  • View: 1245
DOWNLOAD NOW »
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora

Mastering Scientific Computing with R

Mastering Scientific Computing with R

  • Author: Paul Gerrard,Radia M. Johnson
  • Publisher: Packt Publishing Ltd
  • ISBN: 1783555262
  • Category: Computers
  • Page: 432
  • View: 5179
DOWNLOAD NOW »
If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

Efficient Numerical Methods and Information-Processing Techniques for Modeling Hydro- and Environmental Systems

Efficient Numerical Methods and Information-Processing Techniques for Modeling Hydro- and Environmental Systems

  • Author: Reinhard Hinkelmann
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540241461
  • Category: Science
  • Page: 306
  • View: 4349
DOWNLOAD NOW »
Numerical simulation models have become indispensable in hydro- and environmental sciences and engineering. This monograph presents a general introduction to numerical simulation in environment water, based on the solution of the equations for groundwater flow and transport processes, for multiphase and multicomponent flow and transport processes in the subsurface as well as for flow and transport processes in surface waters. It displays in detail the state of the art of discretization and stabilization methods (e.g. finite-difference, finite-element, and finite-volume methods), parallel methods, and adaptive methods as well as fast solvers, with particular focus on explaining the interactions of the different methods. The book gives a brief overview of various information-processing techniques and demonstrates the interactions of the numerical methods with the information-processing techniques, in order to achieve efficient numerical simulations for a wide range of applications in environment water.

Advances in Complex Data Modeling and Computational Methods in Statistics

Advances in Complex Data Modeling and Computational Methods in Statistics

  • Author: Anna Maria Paganoni,Piercesare Secchi
  • Publisher: Springer
  • ISBN: 3319111493
  • Category: Mathematics
  • Page: 209
  • View: 1309
DOWNLOAD NOW »
The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

Problems & Solutions in Scientific Computing

Problems & Solutions in Scientific Computing

With C++ and Java Simulations

  • Author: Willi-Hans Steeb
  • Publisher: World Scientific
  • ISBN: 9789812561251
  • Category: Mathematics
  • Page: 420
  • View: 5142
DOWNLOAD NOW »
Scientific computing is a collection of tools, techniques and theories required to develop and solve mathematical models in science and engineering on a computer. This timely book provides the various skills and techniques needed in scientific computing. The topics range in difficulty from elementary to advanced, and all the latest fields in scientific computing are covered such as matrices, numerical analysis, neural networks, genetic algorithms, etc.Presented in the format of problems and detailed solutions, important concepts and techniques are introduced and developed. Many problems include software simulations. Algorithms have detailed implementations in C++ or Java. This book will prove to be invaluable not only to students and research workers in the fields of scientific computing, but also to teachers of this subject who will find this text useful as a supplement.The topics discussed in this book are part of the e-learning and distance learning courses conducted by the International School of Scientific Computing, South Africa.

Introduction to Computational Models with Python

Introduction to Computational Models with Python

  • Author: Jose M. Garrido
  • Publisher: CRC Press
  • ISBN: 1498712045
  • Category: Computers
  • Page: 466
  • View: 6963
DOWNLOAD NOW »
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 modules. The Python source code and data files are available on the author’s website. The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware tools Programming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithms Python lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under Linux Implementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational models This book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.

Data-Driven Modeling & Scientific Computation

Data-Driven Modeling & Scientific Computation

Methods for Complex Systems & Big Data

  • Author: J. Nathan Kutz
  • Publisher: OUP Oxford
  • ISBN: 019163588X
  • Category: Language Arts & Disciplines
  • Page: 608
  • View: 3473
DOWNLOAD NOW »
The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: · statistics, · time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.

Mastering Numerical Computing with NumPy

Mastering Numerical Computing with NumPy

Master scientific computing and perform complex operations with ease

  • Author: Mert Cuhadaroglu,Umit Mert Cakmak
  • Publisher: Packt Publishing Ltd
  • ISBN: 1788996844
  • Category: Computers
  • Page: 248
  • View: 8441
DOWNLOAD NOW »
Enhance the power of NumPy and start boosting your scientific computing capabilities Key Features Grasp all aspects of numerical computing and understand NumPy Explore examples to learn exploratory data analysis (EDA), regression, and clustering Access NumPy libraries and use performance benchmarking to select the right tool Book Description NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations. What you will learn Perform vector and matrix operations using NumPy Perform exploratory data analysis (EDA) on US housing data Develop a predictive model using simple and multiple linear regression Understand unsupervised learning and clustering algorithms with practical use cases Write better NumPy code and implement the algorithms from scratch Perform benchmark tests to choose the best configuration for your system Who this book is for Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book.

Accuracy and Reliability in Scientific Computing

Accuracy and Reliability in Scientific Computing

  • Author: Bo Einarsson
  • Publisher: SIAM
  • ISBN: 9780898718157
  • Category: Computer programs
  • Page: 338
  • View: 2447
DOWNLOAD NOW »
Numerical software is used to test scientific theories, design airplanes and bridges, operate manufacturing lines, control power plants and refineries, analyze financial derivatives, identify genomes, and provide the understanding necessary to derive and analyze cancer treatments. Because of the high stakes involved, it is essential that results computed using software be accurate, reliable, and robust. Unfortunately, developing accurate and reliable scientific software is notoriously difficult. This book investigates some of the difficulties related to scientific computing and provides insight into how to overcome them and obtain dependable results. The tools to assess existing scientific applications are described, and a variety of techniques that can improve the accuracy and reliability of newly developed applications is discussed. Accuracy and Reliability in Scientific Computing can be considered a handbook for improving the quality of scientific computing. It will help computer scientists address the problems that affect software in general as well as the particular challenges of numerical computation: approximations occurring at all levels, continuous functions replaced by discretized versions, infinite processes replaced by finite ones, and real numbers replaced by finite precision numbers. Divided into three parts, it starts by illustrating some of the difficulties in producing robust and reliable scientific software. Well-known cases of failure are reviewed and the what and why of numerical computations are considered. The second section describes diagnostic tools that can be used to assess the accuracy and reliability of existing scientific applications. In the last section, the authors describe a variety of techniques that can be employed to improve the accuracy and reliability of newly developed scientific applications. The authors of the individual chapters are international experts, many of them members of the IFIP Working Group on Numerical Software.

Datenanalyse mit Python

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

  • Author: Wes McKinney
  • Publisher: O'Reilly
  • ISBN: 3960102143
  • Category: Computers
  • Page: 542
  • View: 5177
DOWNLOAD NOW »
Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Computational Methods for Optimizing Manufacturing Technology: Models and Techniques

Computational Methods for Optimizing Manufacturing Technology: Models and Techniques

Models and Techniques

  • Author: Davim, J. Paulo
  • Publisher: IGI Global
  • ISBN: 1466601299
  • Category: Technology & Engineering
  • Page: 395
  • View: 9863
DOWNLOAD NOW »
"This book contains the latest research developments in manufacturing technology and its optimization, and demonstrates the fundamentals of new computational approaches and the range of their potential application"--Provided by publisher.

Advances in Data-based Approaches for Hydrologic Modeling and Forecasting

Advances in Data-based Approaches for Hydrologic Modeling and Forecasting

  • Author: Bellie Sivakumar,Ronny Berndtsson
  • Publisher: World Scientific
  • ISBN: 9814307971
  • Category: Science
  • Page: 519
  • View: 773
DOWNLOAD NOW »
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.

Transactions on Computational Collective Intelligence X

Transactions on Computational Collective Intelligence X

  • Author: Ngoc Thanh Nguyen
  • Publisher: Springer
  • ISBN: 364238496X
  • Category: Computers
  • Page: 207
  • View: 3367
DOWNLOAD NOW »
These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the Semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This tenth issue contains 13 carefully selected and thoroughly revised contributions.

Intelligent Techniques and Tools for Novel System Architectures

Intelligent Techniques and Tools for Novel System Architectures

  • Author: Panagiotis Chountas,Ilias Petrounias
  • Publisher: Springer Science & Business Media
  • ISBN: 3540776214
  • Category: Mathematics
  • Page: 548
  • View: 8881
DOWNLOAD NOW »
This volume presents new directions and solutions in broadly perceived intelligent systems. An urgent need this volume has occurred as a result of vivid discussions and presentations at the "IEEE-IS’ 2006 – The 2006 Third International IEEE Conference on Intelligent Systems" held in London, UK, September, 2006. This book is a compilation of many valuable inspiring works written by both the conference participants and some other experts in this new and challenging field.

Modeling Methods for Marine Science

Modeling Methods for Marine Science

  • Author: David M. Glover,William J. Jenkins,Scott C. Doney
  • Publisher: Cambridge University Press
  • ISBN: 1139500716
  • Category: Science
  • Page: N.A
  • View: 1982
DOWNLOAD NOW »
This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squares regression, principal component analysis, time series analysis and objective interpolation. The second part deals with modeling techniques: finite differences, stability analysis and optimization. The third part describes case studies of actual ocean models of ever increasing dimensionality and complexity, starting with zero-dimensional models and finishing with three-dimensional general circulation models. Throughout the book hands-on computational examples are introduced using the MATLAB programming language and the principles of scientific visualization are emphasised. Ideal as a textbook for advanced students of oceanography on courses in data analysis and numerical modeling, the book is also an invaluable resource for a broad range of scientists undertaking modeling in chemical, biological, geological and physical oceanography.

Proceedings of the 8th Python in Science Conference

Proceedings of the 8th Python in Science Conference

  • Author: GaeÌl Varoquaux,Stéfan van der Walt,K. Jarrod Millman
  • Publisher: Lulu.com
  • ISBN: 0557232120
  • Category:
  • Page: 92
  • View: 2612
DOWNLOAD NOW »
The proceedings of the 8th annual Python for Scientific Computing conference.

Approximation and Modeling with B-Splines

Approximation and Modeling with B-Splines

  • Author: Klaus HoÓllig,JÓorg HÓorner
  • Publisher: SIAM
  • ISBN: 1611972949
  • Category: Mathematics
  • Page: 214
  • View: 9950
DOWNLOAD NOW »
B-splines are fundamental to approximation and data fitting, geometric modeling, automated manufacturing, computer graphics, and numerical simulation. With an emphasis on key results and methods that are most widely used in practice, this textbook provides a unified introduction to the basic components of B-spline theory: approximation methods (mathematics), modeling techniques (engineering), and geometric algorithms (computer science). A supplemental Web site will provide a collection of problems, some with solutions, slides for use in lectures, and programs with demos.

Quantitative Information Fusion for Hydrological Sciences

Quantitative Information Fusion for Hydrological Sciences

  • Author: Xing Cai,T.-C. Jim Yeh
  • Publisher: Springer Science & Business Media
  • ISBN: 3540753834
  • Category: Science
  • Page: 218
  • View: 1186
DOWNLOAD NOW »
In this rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? Here, two highly qualified scientists edit a volume that takes the angle of computational hydrology and envision one of the science’s future directions – namely, the quantitative integration of high-quality hydrologic field data with geologic, hydrologic, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences.

Soft Computing Methods for Microwave and Millimeter-Wave Design Problems

Soft Computing Methods for Microwave and Millimeter-Wave Design Problems

  • Author: Narendra Chauhan,M.V. Kartikeyan,Ankush Mittal
  • Publisher: Springer Science & Business Media
  • ISBN: 3642255620
  • Category: Computers
  • Page: 114
  • View: 6621
DOWNLOAD NOW »
The growing commercial market of Microwave/ Millimeter wave industry over the past decade has led to the explosion of interests and opportunities for the design and development of microwave components.The design of most microwave components requires the use of commercially available electromagnetic (EM) simulation tools for their analysis. In the design process, the simulations are carried out by varying the design parameters until the desired response is obtained. The optimization of design parameters by manual searching is a cumbersome and time consuming process. Soft computing methods such as Genetic Algorithm (GA), Artificial Neural Network (ANN) and Fuzzy Logic (FL) have been widely used by EM researchers for microwave design since last decade. The aim of these methods is to tolerate imprecision, uncertainty, and approximation to achieve robust and low cost solution in a small time frame. Modeling and optimization are essential parts and powerful tools for the microwave/millimeter wave design. This book deals with the development and use of soft computing methods for tackling challenging design problems in the microwave/millimeter wave domain. The aim in the development of these methods is to obtain the design in small time frame while improving the accuracy of the design for a wide range of applications. To achieve this goal, a few diverse design problems of microwave field, representing varied challenges in the design, such as different microstrip antennas, microwave filters, a microstrip-via and also some critical high power components such as nonlinear tapers and RF-windows are considered as case-study design problems. Different design methodologies are developed for these applications. The presents soft computing methods, their review for microwave/millimeter wave design problems and specific case-study problems to infuse better insight and understanding of the subject.