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Título : An Introduction to R for Quantitative Economics : Graphing, Simulating and Computing Tipo de documento: documento electrónico Autores: Dayal, Vikram ; SpringerLink (Online service) Editorial: New Delhi : Springer India Fecha de publicación: 2015 Otro editor: Imprint: Springer Colección: SpringerBriefs in Economics, ISSN 2191-5504 Número de páginas: XV, 109 p. 79 illus., 9 illus. in color Il.: online resource ISBN/ISSN/DL: 978-81-322-2340-5 Idioma : Inglés (eng) Palabras clave: Computers Computer simulation Statistics Econometrics Economics for Business/Economics/Mathematical Finance/Insurance Simulation and Modeling Computing/Statistics Programs Computing Methodologies Clasificación: 658 Empresas. Organización de empresas Resumen: This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful Nota de contenido: Chapter 1. Introduction -- Chapter 2. R and RStudio -- Chapter 3. Getting data into R -- Chapter 4. Supply and demand -- Chapter 5. Functions -- Chapter 6. The Cobb-Douglas Function -- Chapter 7. Matrices -- Chapter 8. Statistical simulation -- Chapter 9. Anscombe's quartet: graphs can reveal -- Chapter 10. Carbon and forests: graphs and regression -- Chapter 11. Evaluating training -- Chapter 12. The Solow growth model -- Chapter 13. Simulating random walks and shing cycles -- Chapter 14. Basic time series En línea: http://dx.doi.org/10.1007/978-81-322-2340-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35856 An Introduction to R for Quantitative Economics : Graphing, Simulating and Computing [documento electrónico] / Dayal, Vikram ; SpringerLink (Online service) . - New Delhi : Springer India : Imprint: Springer, 2015 . - XV, 109 p. 79 illus., 9 illus. in color : online resource. - (SpringerBriefs in Economics, ISSN 2191-5504) .
ISBN : 978-81-322-2340-5
Idioma : Inglés (eng)
Palabras clave: Computers Computer simulation Statistics Econometrics Economics for Business/Economics/Mathematical Finance/Insurance Simulation and Modeling Computing/Statistics Programs Computing Methodologies Clasificación: 658 Empresas. Organización de empresas Resumen: This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful Nota de contenido: Chapter 1. Introduction -- Chapter 2. R and RStudio -- Chapter 3. Getting data into R -- Chapter 4. Supply and demand -- Chapter 5. Functions -- Chapter 6. The Cobb-Douglas Function -- Chapter 7. Matrices -- Chapter 8. Statistical simulation -- Chapter 9. Anscombe's quartet: graphs can reveal -- Chapter 10. Carbon and forests: graphs and regression -- Chapter 11. Evaluating training -- Chapter 12. The Solow growth model -- Chapter 13. Simulating random walks and shing cycles -- Chapter 14. Basic time series En línea: http://dx.doi.org/10.1007/978-81-322-2340-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35856 Ejemplares
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Título : A Primer on Scientific Programming with Python Tipo de documento: documento electrónico Autores: Langtangen, Hans Petter ; SpringerLink (Online service) Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2011 Colección: Texts in Computational Science and Engineering, ISSN 1611-0994 num. 6 Número de páginas: XXX, 706 p. 72 illus., 28 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-18366-9 Idioma : Inglés (eng) Palabras clave: Mathematics Software engineering Computer programming science mathematics Physics Computational Science and Engineering Programming Techniques Engineering/Programming Operating Systems of Computing Numerical Clasificación: 51 Matemáticas Resumen: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example- and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology, and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background, and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science Nota de contenido: Computing with Formulas -- Loops and Lists -- Functions and Branching -- Input Data and Error Handling -- Array Computing and Curve Plotting -- Files, Strings and Dictionaries -- Introduction to Classes -- Random Numbers and Simple Games -- Object-Oriented Programming -- Sequences and Difference Equations -- Introduction to Discrete Calculus.- Introduction to Differential Equations -- A Complete Differential Equation Project -- Programming of Differential Equations -- Debugging -- Technical Topics -- Bibliography -- Index En línea: http://dx.doi.org/10.1007/978-3-642-18366-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33411 A Primer on Scientific Programming with Python [documento electrónico] / Langtangen, Hans Petter ; SpringerLink (Online service) . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2011 . - XXX, 706 p. 72 illus., 28 illus. in color : online resource. - (Texts in Computational Science and Engineering, ISSN 1611-0994; 6) .
ISBN : 978-3-642-18366-9
Idioma : Inglés (eng)
Palabras clave: Mathematics Software engineering Computer programming science mathematics Physics Computational Science and Engineering Programming Techniques Engineering/Programming Operating Systems of Computing Numerical Clasificación: 51 Matemáticas Resumen: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example- and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology, and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background, and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science Nota de contenido: Computing with Formulas -- Loops and Lists -- Functions and Branching -- Input Data and Error Handling -- Array Computing and Curve Plotting -- Files, Strings and Dictionaries -- Introduction to Classes -- Random Numbers and Simple Games -- Object-Oriented Programming -- Sequences and Difference Equations -- Introduction to Discrete Calculus.- Introduction to Differential Equations -- A Complete Differential Equation Project -- Programming of Differential Equations -- Debugging -- Technical Topics -- Bibliography -- Index En línea: http://dx.doi.org/10.1007/978-3-642-18366-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33411 Ejemplares
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Título : A Primer on Scientific Programming with Python Tipo de documento: documento electrónico Autores: Langtangen, Hans Petter ; SpringerLink (Online service) Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2009 Colección: Texts in Computational Science and Engineering, ISSN 1611-0994 num. 6 Número de páginas: XXVIII, 694 p Il.: online resource ISBN/ISSN/DL: 978-3-642-02475-7 Idioma : Inglés (eng) Palabras clave: Computer science Software engineering programming Mathematics mathematics Physics Science of Computing Computational and Engineering Programming Techniques Engineering/Programming Operating Systems Numerical Clasificación: 51 Matemáticas Resumen: Theaimofthisbookistoteachcomputerprogrammingusingexamples from mathematics and the natural sciences. We have chosen to use the Python programming language because it combines remarkable power with very clean, simple, and compact syntax. Python is easy to learn and very well suited for an introduction to computer programming. Python is also quite similar to Matlab and a good language for doing mathematical computing. It is easy to combine Python with compiled languages, like Fortran, C, and C++, which are widely used languages forscienti?ccomputations.AseamlessintegrationofPythonwithJava is o?ered by a special version of Python called Jython. The examples in this book integrate programming with appli- tions to mathematics, physics, biology, and ?nance. The reader is - pected to have knowledge of basic one-variable calculus as taught in mathematics-intensive programs in high schools. It is certainly an - vantage to take a university calculus course in parallel, preferably c- taining both classical and numerical aspects of calculus. Although not strictly required, a background in high school physics makes many of the examples more meaningful Nota de contenido: Computing with Formulas -- Basic Constructions -- Input Data and Error Handling -- Array Computing and Curve Plotting -- Sequences and Difference Equations -- Files, Strings, and Dictionaries -- to Classes -- Random Numbers and Simple Games -- Object-Oriented Programming En línea: http://dx.doi.org/10.1007/978-3-642-02475-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34071 A Primer on Scientific Programming with Python [documento electrónico] / Langtangen, Hans Petter ; SpringerLink (Online service) . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2009 . - XXVIII, 694 p : online resource. - (Texts in Computational Science and Engineering, ISSN 1611-0994; 6) .
ISBN : 978-3-642-02475-7
Idioma : Inglés (eng)
Palabras clave: Computer science Software engineering programming Mathematics mathematics Physics Science of Computing Computational and Engineering Programming Techniques Engineering/Programming Operating Systems Numerical Clasificación: 51 Matemáticas Resumen: Theaimofthisbookistoteachcomputerprogrammingusingexamples from mathematics and the natural sciences. We have chosen to use the Python programming language because it combines remarkable power with very clean, simple, and compact syntax. Python is easy to learn and very well suited for an introduction to computer programming. Python is also quite similar to Matlab and a good language for doing mathematical computing. It is easy to combine Python with compiled languages, like Fortran, C, and C++, which are widely used languages forscienti?ccomputations.AseamlessintegrationofPythonwithJava is o?ered by a special version of Python called Jython. The examples in this book integrate programming with appli- tions to mathematics, physics, biology, and ?nance. The reader is - pected to have knowledge of basic one-variable calculus as taught in mathematics-intensive programs in high schools. It is certainly an - vantage to take a university calculus course in parallel, preferably c- taining both classical and numerical aspects of calculus. Although not strictly required, a background in high school physics makes many of the examples more meaningful Nota de contenido: Computing with Formulas -- Basic Constructions -- Input Data and Error Handling -- Array Computing and Curve Plotting -- Sequences and Difference Equations -- Files, Strings, and Dictionaries -- to Classes -- Random Numbers and Simple Games -- Object-Oriented Programming En línea: http://dx.doi.org/10.1007/978-3-642-02475-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34071 Ejemplares
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Título : Computational Statistics Tipo de documento: documento electrónico Autores: Gentle, James E ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2009 Colección: Statistics and Computing, ISSN 1431-8784 Número de páginas: XXII, 728 p Il.: online resource ISBN/ISSN/DL: 978-0-387-98144-4 Idioma : Inglés (eng) Palabras clave: Mathematics Computer science Numerical analysis Data mining mathematics Probabilities Statistics Probability Theory and Stochastic Processes Computational Analysis of Computing Computing/Statistics Programs Numeric Mining Knowledge Discovery Clasificación: 51 Matemáticas Resumen: Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra Nota de contenido: Preliminaries -- Mathematical and Statistical Preliminaries -- Statistical Computing -- Computer Storage and Arithmetic -- Algorithms and Programming -- Approximation of Functions and Numerical Quadrature -- Numerical Linear Algebra -- Solution of Nonlinear Equations and Optimization -- Generation of Random Numbers -- Methods of Computational Statistics -- Graphical Methods in Computational Statistics -- Tools for Identification of Structure in Data -- Estimation of Functions -- Monte Carlo Methods for Statistical Inference -- Data Randomization, Partitioning, and Augmentation -- Bootstrap Methods -- Exploring Data Density and Relationships -- Estimation of Probability Density Functions Using Parametric Models -- Nonparametric Estimation of Probability Density Functions -- Statistical Learning and Data Mining -- Statistical Models of Dependencies En línea: http://dx.doi.org/10.1007/978-0-387-98144-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33931 Computational Statistics [documento electrónico] / Gentle, James E ; SpringerLink (Online service) . - New York, NY : Springer New York, 2009 . - XXII, 728 p : online resource. - (Statistics and Computing, ISSN 1431-8784) .
ISBN : 978-0-387-98144-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer science Numerical analysis Data mining mathematics Probabilities Statistics Probability Theory and Stochastic Processes Computational Analysis of Computing Computing/Statistics Programs Numeric Mining Knowledge Discovery Clasificación: 51 Matemáticas Resumen: Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra Nota de contenido: Preliminaries -- Mathematical and Statistical Preliminaries -- Statistical Computing -- Computer Storage and Arithmetic -- Algorithms and Programming -- Approximation of Functions and Numerical Quadrature -- Numerical Linear Algebra -- Solution of Nonlinear Equations and Optimization -- Generation of Random Numbers -- Methods of Computational Statistics -- Graphical Methods in Computational Statistics -- Tools for Identification of Structure in Data -- Estimation of Functions -- Monte Carlo Methods for Statistical Inference -- Data Randomization, Partitioning, and Augmentation -- Bootstrap Methods -- Exploring Data Density and Relationships -- Estimation of Probability Density Functions Using Parametric Models -- Nonparametric Estimation of Probability Density Functions -- Statistical Learning and Data Mining -- Statistical Models of Dependencies En línea: http://dx.doi.org/10.1007/978-0-387-98144-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33931 Ejemplares
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Título : Advanced Computing Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Michael Bader ; Bungartz, Hans-Joachim ; Weinzierl, Tobias Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Lecture Notes in Computational Science and Engineering, ISSN 1439-7358 num. 93 Número de páginas: XII, 240 p. 117 illus., 81 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-38762-3 Idioma : Inglés (eng) Palabras clave: Mathematics Numerical analysis Computer simulation mathematics Physics Applied Engineering Computational and Analysis Science Simulation Modeling Numeric Computing Appl.Mathematics/Computational Methods of Clasificación: 51 Matemáticas Resumen: This proceedings volume collects review articles that summarize research conducted at the Munich Centre of Advanced Computing (MAC) from 2008 to 2012. The articles address the increasing gap between what should be possible in Computational Science and Engineering due to recent advances in algorithms, hardware, and networks, and what can actually be achieved in practice; they also examine novel computing architectures, where computation itself is a multifaceted process, with hardware awareness or ubiquitous parallelism due to many-core systems being just two of the challenges faced. Topics cover both the methodological aspects of advanced computing (algorithms, parallel computing, data exploration, software engineering) and cutting-edge applications from the fields of chemistry, the geosciences, civil and mechanical engineering, etc., reflecting the highly interdisciplinary nature of the Munich Centre of Advanced Computing Nota de contenido: A Review of the Finite Cell Method for Nonlinear Structural Analysis of Complex CAD and Image-based Geometric Models: Dominik Schillinger, Quanji Cai, Ralf-Peter Mundani, and Ernst Rank -- Immersed Boundary Methods for Fluid-Structure Interaction and Shape Optimization within an FEM-based PDE Toolbox: Janos Benk, Hans-Joachim Bungartz, Miriam Mehl, and Michael Ulbrich -- Numerical simulation of transport in porous media: some problems from micro to macro scale: Quanji Cai Sheema Kooshapur Michael Manhart, Ralf-Peter Mundani, Ernst Rank, Andreas Springer, Boris Vexler -- Optimal Control of Partially Miscible Two-Phase Flow with Applications to Subsurface CO2 Sequestration: Moritz Simon and Michael Ulbrich -- A Newton-CG Method for Full-Waveform Inversion in a Coupled Solid-Fluid System: Christian Boehm and Michael Ulbrich -- Advances in the Parallelisation of Software for Quantum Chemistry Applications: Martin Roderus, Alexei Matveev, Hans-Joachim Bungartz and Notker Rösch -- Designing Spacecraft High Performance Computing Architectures: Fisnik Kraja, Georg Acher, Arndt Bode -- Requirements Engineering for Computational Seismology Software: Yang Li, Bernd Bruegge, Simon Stähler, Nitesh Narayan, and Heiner Igel -- A High-Performance Interactive Computing Framework for Engineering Applications: Jovana Kneževi´c, Ralf-Peter Mundani, Ernst Rank -- A Framework for the Interactive Handling of High-Dimensional Simulation Data in Complex Geometries: A. Benzina, G. Buse, D. Butnaru, A. Murarasu, M. Treib, V. Varduhn, R.-P. Mundani -- Experiences with a Flexibly Reconfigurable Visualization System on Software Development and Workplace Ergonomics: Marcus Tönnis, Amal Benzina, Gudrun Klinker En línea: http://dx.doi.org/10.1007/978-3-642-38762-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32586 Advanced Computing [documento electrónico] / SpringerLink (Online service) ; Michael Bader ; Bungartz, Hans-Joachim ; Weinzierl, Tobias . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013 . - XII, 240 p. 117 illus., 81 illus. in color : online resource. - (Lecture Notes in Computational Science and Engineering, ISSN 1439-7358; 93) .
ISBN : 978-3-642-38762-3
Idioma : Inglés (eng)
Palabras clave: Mathematics Numerical analysis Computer simulation mathematics Physics Applied Engineering Computational and Analysis Science Simulation Modeling Numeric Computing Appl.Mathematics/Computational Methods of Clasificación: 51 Matemáticas Resumen: This proceedings volume collects review articles that summarize research conducted at the Munich Centre of Advanced Computing (MAC) from 2008 to 2012. The articles address the increasing gap between what should be possible in Computational Science and Engineering due to recent advances in algorithms, hardware, and networks, and what can actually be achieved in practice; they also examine novel computing architectures, where computation itself is a multifaceted process, with hardware awareness or ubiquitous parallelism due to many-core systems being just two of the challenges faced. Topics cover both the methodological aspects of advanced computing (algorithms, parallel computing, data exploration, software engineering) and cutting-edge applications from the fields of chemistry, the geosciences, civil and mechanical engineering, etc., reflecting the highly interdisciplinary nature of the Munich Centre of Advanced Computing Nota de contenido: A Review of the Finite Cell Method for Nonlinear Structural Analysis of Complex CAD and Image-based Geometric Models: Dominik Schillinger, Quanji Cai, Ralf-Peter Mundani, and Ernst Rank -- Immersed Boundary Methods for Fluid-Structure Interaction and Shape Optimization within an FEM-based PDE Toolbox: Janos Benk, Hans-Joachim Bungartz, Miriam Mehl, and Michael Ulbrich -- Numerical simulation of transport in porous media: some problems from micro to macro scale: Quanji Cai Sheema Kooshapur Michael Manhart, Ralf-Peter Mundani, Ernst Rank, Andreas Springer, Boris Vexler -- Optimal Control of Partially Miscible Two-Phase Flow with Applications to Subsurface CO2 Sequestration: Moritz Simon and Michael Ulbrich -- A Newton-CG Method for Full-Waveform Inversion in a Coupled Solid-Fluid System: Christian Boehm and Michael Ulbrich -- Advances in the Parallelisation of Software for Quantum Chemistry Applications: Martin Roderus, Alexei Matveev, Hans-Joachim Bungartz and Notker Rösch -- Designing Spacecraft High Performance Computing Architectures: Fisnik Kraja, Georg Acher, Arndt Bode -- Requirements Engineering for Computational Seismology Software: Yang Li, Bernd Bruegge, Simon Stähler, Nitesh Narayan, and Heiner Igel -- A High-Performance Interactive Computing Framework for Engineering Applications: Jovana Kneževi´c, Ralf-Peter Mundani, Ernst Rank -- A Framework for the Interactive Handling of High-Dimensional Simulation Data in Complex Geometries: A. Benzina, G. Buse, D. Butnaru, A. Murarasu, M. Treib, V. Varduhn, R.-P. Mundani -- Experiences with a Flexibly Reconfigurable Visualization System on Software Development and Workplace Ergonomics: Marcus Tönnis, Amal Benzina, Gudrun Klinker En línea: http://dx.doi.org/10.1007/978-3-642-38762-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32586 Ejemplares
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