ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Monograph available for loan
    Monograph available for loan
    Berlin : Springer
    Associated volumes
    Call number: AWI S4-19-91819
    In: Texts in computational science and engineering, 3
    Type of Medium: Monograph available for loan
    Pages: XXIV, 750 Seiten , Illustrationen
    Edition: third edition, corrected 2nd printing 2009
    ISBN: 3540739157 , 9783540739159 , 9783540739166 (electronic)
    Series Statement: Texts in computational science and engineering 3
    Language: English
    Note: Table of Contents 1 Introduction 1.1 Scripting versus Traditional Programming 1.1.1 Why Scripting is Useful in Computational Science 1.1.2 Classification of Programming Languages 1.1.3 Productive Pairs of Programming Languages 1.1.4 Gluing Existing Applications 1.1.5 Scripting Yields Shorter Code 1.1.6 Efficiency 1.1.7 Type-Specification (Declaration) of Variables 1.1.8 Flexible Function Interfaces 1.1.9 Interactive Computing 1.1.10 Creating Code at Run Time 1.1.11 Nested Heterogeneous Data Structures 1.1.12 GUI Programming 1.1.13 Mixed Language Programming 1.1.14 When to Choose a Dynamically Typed Language 1.1.15 Why Python? 1.1.16 Script or Program? 1.2 Preparations for Working with This Book 2 Getting Started with Python Scripting 2.1 A Scientific Hello World Script 2.1.1 Executing Python Scripts 2.1.2 Dissection of the Scientific Hello World Script 2.2 Working with Files and Data 2.2.1 Problem Specification 2.2.2 The Complete Code 2.2.3 Dissection 2.2.4 Working with Files in Memory 2.2.5 Array Computing 2.2.6 Interactive Computing and Debugging 2. 2.7 Efficiency Measurements 2.2.8 Exercises 2.3 Gluing Stand-Alone Applications 2.3.1 The Simulation Code 2.3.2 Using Gnuplot to Visualize Curves 2.3.3 Functionality of the Script 2.3.4 The Complete Code 2.3.5 Dissection 2.3.6 Exercises 2.4 Conducting Numerical Experiments 2.4.1 Wrapping a Loop Around Another Script 2.4.2 Generating an HTML Report 2.4.3 Making Animations 2.4.4 Varying Any Parameter 2.5 File Format Conversion 2.5.1 A Simple Read/Write Script 2.5.2 Storing Data in Dictionaries and Lists 2.5.3 Making a Module with Functions 2.5.4 Exercises 3 Basic Python 3.1 Introductory Topics 3.1.1 Recommended Python Documentation 3.1.2 Control Statements 3.1.3 Running Applications 3.1.4 File Reading and Writing 3.1.5 Output Formatting 3.2 Variables of Different Types 3.2.1 Boolean Types 3.2.2 The None Variable 3.2.3 Numbers and Numerical Expressions 3.2.4 Lists and Tuples 3.2.5 Dictionaries 3.2.6 Splitting and Joining Text 3.2.7 String Operations 3.2.8 Text Processing 3.2.9 The Basics of a Python Class 3.2.10 Copy and Assignment 3.2.11 Determining a Variable's Type 3.2.12 Exercises 3.3 Functions 3.3.1 Keyword Arguments 3.3.2 Doc Strings 3.3.3 Variable Number of Arguments 3.3.4 Call by Reference 3.3.5 Treatment of Input and Output Arguments 3.3.6 Function Objects 3.4 Working with Files and Directories 3.4.1 Listing Files in a Directory 3.4.2 Testing File Types 3.4.3 Removing Files and Directories 3.4.4 Copying and Renaming Files 3.4.5 Splitting Pathnames 3.4.6 Creating and Moving to Directories 3.4.7 Traversing Directory Trees 3.4.8 Exercises 4 Numerical Computing in Python 4.1 A Quick NumPy Primer 4.1.1 Creating Arrays 4.1.2 Array Indexing 4.1.3 Loops over Arrays 4.1.4 Array Computations 4.1.5 More Array Functionality 4.1.6 Type Testing 4.1.7 Matrix Objects 4.1.8 Exercises 4.2 Vectorized Algorithms 4.2.1 From Scalar to Array in Function Arguments 4.2.2 Slicing 4.2.3 Exercises 4.3 More Advanced Array Computing 4.3.1 Random Numbers 4.3.2 Linear Algebra 4.3.3 Plotting 4.3.4 Example: Curve Fitting 4.3.5 Arrays on Structured Grids 4.3.6 File I/O with NumPy Arrays 4.3.7 Functionality in the Numpyutils Module 4.3.8 Exercises 4.4 Other Tools for Numerical Computations 4.4.1 The ScientificPython Package 4.4.2 The SciPy Package 4.4.3 The Python- Matlab Interface 3 4.4.4 Symbolic Computing in Python 4.4.5 Some Useful Python Modules 5 Combining Python with Fortran, C, and C++ 5.1 About Mixed Language Programming 5.1.1 Applications of Mixed Language Programming 5.1.2 Calling C from Python 5.1.3 Automatic Generation of Wrapper Code 5.2 Scientific Hello World Examples 5.2.1 Combining Python and Fortran 5.2.2 Combining Python and C 5.2.3 Combining Python and C++ Functions 5.2.4 Combining Python and C++ Classes 5.2.5 Exercises 5.3 A Simple Computational Steering Example 5.3.1 Modified Time Loop for Repeated Simulations 5.3.2 Creating a P ython Interface 5.3.3 The Steering Python Script 5.3.4 Equipping the Steering Script with a GUI 5.4 Scripting Interfaces to Large Libraries 6 Introduction to GUI Programming 6.1 Scientific Hello World GUI 6.1.1 Introductory Topics 6.1.2 The First Python/Tkinter Encounter 6.1.3 Binding Events 6.1.4 Changing the Layout 6.1.5 The Final Scientific Hello World GUI 6.1.6 An Alternative to Tkinter Variables 6.1.7 About the Pack Command 6.1.8 An Introduction to the Grid Geometry Manager 6.1.9 Implementing a GUI as a Class 6.1.10 A Simple Graphical Function Evaluator 6.1.11 Exercises 6.2 Adding GUis to Scripts 6.2.1 A Simulation and Visualization Script with a GUI 6.2.2 Improving the Layout 6.2.3 Exercises 6.3 A List of Common Widget Operations 6.3.1 Frame 6.3.2 Label 6.3.3 Button 6.3.4 Text Entry 6.3.5 Balloon Help 6.3.6 Option Menu 6.3.7 Slider 6.3.8 Check Button 6.3.9 Making a Simple Megawidget 6.3.10 Menu Bar 6.3.11 List Data 6.3.12 Listbox 6.3.13 Radio Button 6.3.14 Combo Box 6.3.15 Message Box 6.3.16 User-Defined Dialogs 6.3.17 Color-Picker Dialogs 6.3.18 File Selection Dialogs 6.3.19 Toplevel 6.3.20 Some Other Types of Widgets 6.3.21 Adapting Widgets to the User's Resize Actions 6.3.22 Customizing Fonts and Colors 6.3.23 Widget Overview 6.3.24 Exercises 7 Web Interfaces and CGI Programming 7.1 Introductory CGI Scripts 7.1.1 Web Forms and CGI Scripts 7.1.2 Generating Forms in CGI Scripts 7.1.3 Debugging CGI Scripts 7.1.4 A General Shell Script Wrapper for CGI Scripts 7.1.5 Security Issues 7.2 Adding Web Interfaces to Scripts 7.2.1 A Class for Form Parameters 7.2.2 Calling Other Programs 7.2.3 Running Simulations 7.2.4 Getting a CGI Script to Work 7.2.5 Using Web Applications from Scripts 7.2.6 Exercises 8 Advanced Python 8.1 Miscellaneous Topics 8.1.1 Parsing Command-Line Arguments 8.1.2 Platform-Dependent Operations 8.1.3 Run-Time Generation of Code 8.1.4 Exercises 8.2 Regular Expressions and Text Processing 8.2.1 Motivation 8.2.2 Special Characters 8.2.3 Regular Expressions for Real Numbers 8.2.4 Using Groups to Extract Parts of a Text 8.2.5 Extracting Interval Limits 8.2.6 Extracting Multiple Matches 8.2.7 Splitting Text 8.2.8 Pattern-Matching Modifiers 8.2.9 Substitution and Backreferences 8.2.10 Example: Swapping Arguments in Function Calls 8.2.11 A General Substitution Script 8.2.12 Debugging Regular Expressions 8.2.13 Exercises 8.3 Tools for Handling Data in Files 8.3.1 Writing and Reading Python Data Structures 8.3.2 Pickling Objects 8.3.3 Shelving Objects 8.3.4 Writing and Reading Zip and Tar Archive Files 8.3.5 Downloading Internet Files 8.3.6 Binary Input/Output 8.3.7 Exercises 8.4 A Database for NumPy Arrays 8.4.1 The Structure of the Database 8.4.2 Pickling 8.4.3 Formatted ASCII Storage 8.4.4 Shelving 8.4.5 Comparing the Various Techniques 8.5 Scripts Involving Local and Remote Hosts 8.5.1 Secure Shell Commands 8.5.2 Distributed Simulation and Visualization 8.5.3 Client/Server Programming 8.5.4 Threads 8.6 Classes 8.6.1 Class Programming 8.6.2 Checking the Class Type 8.6.3 Private Data 8.6.4 Static Data 8.6.5 Special Attributes 8.6.6 Special Methods 8.6.7 Multiple Inheritance 8.6.8 Using a Class as a C-like Structure 8.6.9 Attribute Access via String Names 8.6.10 New-Style Classes 8.6.11 Implementing Get/Set Functions via Properties 8.6.12 Subclassing Built-in Types 8.6.13 Building Class Interfaces at Run Time 8.6.14 Building Flexible Class Interfaces 8.6.15 Exercises 8.7 Scope of Variables 8.7.1 Global, Local, and Class Variables 8.7.2 Nested Functions 8.7.3 Dictionaries of Variables in Namespaces 8.8 Exceptions 8.8.1 Handling Exceptions 8.8.2 Raising Exceptions 8.9 Iterators 8.9.1 Constructing an Iterator 8.9.2 A Pointwise Grid Iterator 8.9.3 A Vectorized Grid Iterator 8.9.4 Generators 8.
    Location: AWI Reading room
    Branch Library: AWI Library
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Monograph available for loan
    Monograph available for loan
    Berlin : Springer
    Associated volumes
    Call number: AWI S4-18-91822
    In: Texts in computational science and engineering, 6
    Type of Medium: Monograph available for loan
    Pages: XXXI, 922Seiten , Illustrationen, graphische Darstellungen
    Edition: Fifth edition
    ISBN: 9783662498873 , 9783662498866
    Series Statement: Texts in computational science and engineering 6
    Language: English
    Note: Contents: 1 Computing with Formulas. - 1.1 The First Programming Encounter: a Formula. - 1.1.1 Using a Program as a Calculator. - 1.1.2 About Programs and Programming. - 1.1.3 Tools for Writing Programs. - 1.1.4 Writing and Running Your First Python Program. - 1.1.5 Warning About Typing Program Text. - 1.1.6 Verifying the Result. - 1.1.7 Using Variables. - 1.1.8 Names of Variables. - 1.1.9 Reserved Words in Python. - 1.1.10 Comments. - 1.1.11 Formatting Text and Numbers. - 1.2 Computer Science Glossary. - 1.3 Another Formula: Celsius-Fahrenheit Conversion. - 1.3.1 Potential Error: Integer Division. - 1.3.2 Objects in Python. - 1.3.3 Avoiding Integer Division. - 1.3.4 Arithmetic Operators and Precedence. - 1.4 Evaluating Standard Mathematical Functions. - 1.4.1 Example: Using the Square Root Function. - 1.4.2 Example: Computing with sinh x. - 1.4.3 A First Glimpse of Rounding Errors. - 1.5 Interactive Computing. - 1.5.1 Using the Python Shell. - 1.5.2 Type Conversion. - 1.5.3 IPython. - 1.6 Complex Numbers. - 1.6.1 Complex Arithmetics in Python. - 1.6.2 Complex Functions in Python. - 1.6.3 Unified Treatment of Complex and Real Functions. - 1.7 Symbolic Computing. - 1.7.1 Basic Differentiation and Integration. - 1.7.2 Equation Solving. - 1.7.3 Taylor Series and More. - 1.8 Summary. - 1.8.1 Chapter Topics. - 1.8.2 Example: Trajectory of a Ball. - 1.8.3 About Typesetting Conventions in This Book. - 1.9 Exercises. - 2 Loops and Lists. - 2.1 While Loops. - 2.1.1 A Naive Solution. - 2.1.2 While Loops. - 2.1.3 Boolean Expressions. - 2.1.4 Loop Implementation of a Sum. - 2.2 Lists. - 2.2.1 Basic List Operations. - 2.2.2 For Loops. - 2.3 Alternative Implementations with Lists and Loops. - 2.3.1 While Loop Implementation of a for Loop. - 2.3.2 The Range Construction. - 2.3.3 For Loops with List Indices. - 2.3.4 Changing List Elements. - 2.3.5 List Comprehension. - 2.3.6 Traversing Multiple Lists Simultaneously. - 2.4 Nested Lists. - 2.4.1 A table as a List of Rows or Columns. - 2.4.2 Printing Objects. - 2.4.3 Extracting Sublists. - 2.4.4 Traversing Nested Lists. - 2.5 Tuples. - 2.6 Summary. - 2.6.1 Chapter Topics. - 2.6.2 Example: Analyzing List Data. - 2.6.3 How to Find More Python Information. - 2.7 Exercises. - 3 Functions and Branching. - 3.1 Functions. - 3.1.1 Mathematical Functions as Python Functions. - 3.1.2 Understanding the Program Flow. - 3.1.3 Local and Global Variables. - 3.1.4 Multiple Arguments. - 3.1.5 Function Argument or Global Variable?. - 3.1.6 Beyond Mathematical Functions. - 3.1.7 Multiple Return Values. - 3.1.8 Computing Sums. - 3.1.9 Functions with No Return Values. - 3.1.10 Keyword Arguments. - 3.1.11 Doc Strings. - 3.1.12 Functions as Arguments to Functions. - 3.1.13 The Main Program. - 3.1.14 Lambda Functions. - 3.2 Branching. - 3.2.1 If-else Blocks. - 3.2.2 Inline if Tests. - 3.3 Mixing Loops, Branching, and Functions in Bioinformatics Examples. - 3.3.1 Counting Letters in DNA Strings. - 3.3.2 Efficiency Assessment. - 3.3.3 Verifying the Implementations. - 3.4 Summary. - 3.4.1 Chapter Topics. - 3.4.2 Example: Numerical Integration. - 3.5 Exercises. - 4 User Input and Error Handling. - 4.1 Asking Questions and Reading Answers. - 4.1.1 Reading Keyboard Input. - 4.2 Reading from the Command Line. - 4.2.1 Providing Input on the Command Line. - 4.2.2 A Variable Number of Command-Line Arguments. - 4.2.3 More on Command-Line Arguments. - 4.3 Turning User Text into Live Objects. - 4.3.1 The Magic Eval Function. - 4.3.2 The Magic Exec Function. - 4.3.3 Turning String Expressions into Functions. - 4.4 Option-Value Pairs on the Command Line. - 4.4.1 Basic Usage of the Argparse Module. - 4.4.2 Mathematical Expressions as Values. - 4.5 Reading Data from File. - 4.5.1 Reading a File Line by Line. - 4.5.2 Alternative Ways of Reading a File. - 4.5.3 Reading a Mixture of Text and Numbers. - 4.6 Writing Data to File. - 4.6.1 Example: Writing a Table to File. - 4.6.2 Standard Input and Output as File Objects. - 4.6.3 What is a File, Really?. - 4.7 Handling Errors. - 4.7.1 Exception Handling. - 4.7.2 Raising Exceptions. - 4.8 A Glimpse of Graphical User Interfaces. - 4.9 Making Modules. - 4.9.1 Example: Interest on Bank Deposits. - 4.9.2 Collecting Functions in a Module File. - 4.9.3 Test Block. - 4.9.4 Verification of the Module Code. - 4.9.5 Getting Input Data. - 4.9.6 Doc Strings in Modules. - 4.9.7 Using Modules. - 4.9.8 Distributing Modules. - 4.9.9 Making Software Available on the Internet. - 4.10 Making Code for Python 2 and 3. - 4.10.1 Basic Differences Between Python 2 and 3. - 4.10.2 Turning Python 2 Code into Python 3 Code. - 4.11 Summary. - 4.11.1 Chapter Topics. - 4.11.2 Example: Bisection Root Finding. - 4.12 Exercises. - 5 Array Computing and Curve Plotting. - 5.1 Vectors. - 5.1.1 The Vector Concept. - 5.1.2 Mathematical Operations on Vectors. - 5.1.3 Vector Arithmetics and Vector Functions. - 5.2 Arrays in Python Programs. - 5.2.1 Using Lists for Collecting Function Data. - 5.2.2 Basics of Numerical Python Arrays. - 5.2.3 Computing Coordinates and Function Values. - 5.2.4 Vectorization. - 5.3 Curve Plotting. - 5.3.1 MATLAB-Style Plotting with Matplotlib. - 5.3.2 Matplotlib; Pyplot Prefix. - 5.3.3 SciTools and Easyviz. - 5.3.4 Making Animations. - 5.3.5 Making Videos. - 5.3.6 Curve Plots in Pure Text. - 5.4 Plotting Difficulties. - 5.4.1 Piecewisely Defined Functions. - 5.4.2 Rapidly Varying Functions. - 5.5 More Advanced Vectorization of Functions. - 5.5.1 Vectorization of StringFunction Objects. - 5.5.2 Vectorization of the Heaviside Function. - 5.5.3 Vectorization of a Hat Function. - 5.6 More on Numerical Python Arrays. - 5.6.1 Copying Arrays. - 5.6.2 In-Place Arithmetics. - 5.6.3 Allocating Arrays. - 5.6.4 Generalized Indexing. - 5.6.5 Testing for the Array Type. - 5.6.6 Compact Syntax for Array Generation. - 5.6.7 Shape Manipulation. - 5.7 High-Performance Computing with Arrays. - 5.7.1 Scalar Implementation. - 5.7.2 Vectorized Implementation. - 5.7.3 Memory-Saving Implementation. - 5.7.4 Analysis of Memory Usage. - 5.7.5 Analysis of the CPU Time. - 5.8 Higher-Dimensional Arrays. - 5.8.1 Matrices and Arrays. - 5.8.2 Two-Dimensional Numerical Python Arrays. - 5.8.3 Array Computing. - 5.8.4 Matrix Objects. - 5.9 Some Common Linear Algebra Operations. - 5.9.1 Inverse, Determinant, and Eigenvalues. - 5.9.2 Products. - 5.9.3 Norms. - 5.9.4 Sum and Extreme Values. - 5.9.5 Indexing. - 5.9.6 Transpose and Upper/Lower Triangular Parts. - 5.9.7 Solving Linear Systems. - 5.9.8 Matrix Row and Column Operations. - 5.9.9 Computing the Rank of a Matrix. - 5.9.10 Symbolic Linear Algebra. - 5.10 Plotting of Scalar and Vector Fields. - 5.10.1 Installation. - 5.10.2 Surface Plots. - 5.10.3 Parameterized Curve. - 5.10.4 Contour Lines. - 5.10.5 The Gradient Vector Field. - 5.11 Matplotlib. - 5.11.1 Surface Plots. - 5.11.2 Contour Plots. - 5.11.3 Vector Field Plots. - 5.12 Mayavi. - 5.12.1 Surface Plots. - 5.12.2 Contour Plots. - 5.12.3 Vector Field Plots. - 5.12.4 A 3D Scalar Field and Its Gradient Field. - 5.12.5 Animations. - 5.13 Summary. - 5.13.1 Chapter Topics. - 5.13.2 Example: Animating a Function. - 5.14 Exercises. - 6 Dictionaries and Strings. - 6.1 Dictionaries. - 6.1.1 Making Dictionaries. - 6.1.2 Dictionary Operations. - 6.1.3 Example: Polynomials as Dictionaries. - 6.1.4 Dictionaries with Default Values and Ordering. - 6.1.5 Example: Storing File Data in Dictionaries. - 6.1.6 Example: Storing File Data in Nested Dictionaries. - 6.1.7 Example: Reading and Plotting Data Recorded at Specific Dates. - 6.2 Strings. - 6.2.1 Common Operations on Strings. - 6.2.2 Example: Reading Pairs of Numbers. - 6.2.3 Example: Reading Coordinates. - 6.3 Reading Data fromWeb Pages. - 6.3.1 About Web Pages. - 6.3.2 How to Access Web Pages
    Location: AWI Reading room
    Branch Library: AWI Library
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Keywords: Mathematics ; Computer programming ; Software engineering ; Computer mathematics ; Mathematics ; Computational Science and Engineering ; Programming Techniques ; Software Engineering ; Numerical and Computational Physics, Simulation ; Mathematical and Computational Engineering
    Description / Table of Contents: Preface --- Algorithms and implementations --- Analysis --- Generalizations --- Models --- Scientific Software Engineering --- References --- Index.
    Pages: Online-Ressource (XIV, 200 pages) , 29 illustrations
    ISBN: 9783319294391
    Language: English
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    Unknown
    Cham : Birkhäuser
    Keywords: Topological Groups ; Harmonic analysis ; Functional analysis ; Topological Groups, Lie Groups ; Abstract Harmonic Analysis ; Functional Analysis ; Mathematical Physics
    Description / Table of Contents: Preface --- Introduction --- Notation and conventions --- 1 Preliminaries on Lie groups --- 2 Quantization on compact Lie groups --- 3 Homogeneous Lie groups --- 4 Rockland operators and Sobolev spaces --- 5 Quantization on graded Lie groups --- 6 Pseudo-differential operators on the Heisenberg group --- A Miscellaneous --- B Group C* and von Neumann algebras --- Schrödinger representations and Weyl quantization --- Explicit symbolic calculus on the Heisenberg group --- List of quantizations --- Bibliography --- Index
    Pages: Online-Ressource (XIII, 557 pages) , 1 illustrations in color
    ISBN: 9783319295589
    Language: English
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 5
    Publication Date: 2024-04-14
    Description: computer science; algorithms; visualization; software; programming
    Keywords: computer science ; algorithms ; visualization ; software ; programming ; thema EDItEUR::U Computing and Information Technology::UY Computer science ; thema EDItEUR::U Computing and Information Technology::UY Computer science
    Language: English
    Format: image/jpeg
    Format: image/jpeg
    Format: image/jpeg
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 6
    Publication Date: 2024-04-14
    Description: Computational Science and Engineering; Numerical Analysis;
    Keywords: Computational Science and Engineering ; Numerical Analysis ; thema EDItEUR::U Computing and Information Technology::UY Computer science
    Language: English
    Format: image/jpeg
    Format: image/jpeg
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 7
    facet.materialart.
    Unknown
    Springer Nature | Springer International Publishing
    Publication Date: 2024-04-04
    Description: The book serves both as a reference for various scaled models with corresponding dimensionless numbers, and as a resource for learning the art of scaling. A special feature of the book is the emphasis on how to create software for scaled models, based on existing software for unscaled models. Scaling (or non-dimensionalization) is a mathematical technique that greatly simplifies the setting of input parameters in numerical simulations. Moreover, scaling enhances the understanding of how different physical processes interact in a differential equation model. Compared to the existing literature, where the topic of scaling is frequently encountered, but very often in only a brief and shallow setting, the present book gives much more thorough explanations of how to reason about finding the right scales. This process is highly problem dependent, and therefore the book features a lot of worked examples, from very simple ODEs to systems of PDEs, especially from fluid mechanics. The text is easily accessible and example-driven. The first part on ODEs fits even a lower undergraduate level, while the most advanced multiphysics fluid mechanics examples target the graduate level. The scientific literature is full of scaled models, but in most of the cases, the scales are just stated without thorough mathematical reasoning. This book explains how the scales are found mathematically. This book will be a valuable read for anyone doing numerical simulations based on ordinary or partial differential equations.
    Keywords: Ordinary Differential Equations ; Partial Differential Equations ; Mathematical Modeling and Industrial Mathematics ; Computational Science and Engineering ; Simulation and Modeling ; Analysis ; Computer Science ; scaling ; non-dimensionalization ; dimensionless numbers ; fluid mechanics ; multiphysics models ; Differential calculus & equations ; Mathematical modelling ; Maths for engineers ; Maths for scientists ; Computer modelling & simulation ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKJ Differential calculus and equations ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling ; thema EDItEUR::P Mathematics and Science::PD Science: general issues::PDE Maths for scientists ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYM Computer modelling and simulation
    Language: English
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 8
    facet.materialart.
    Unknown
    Springer Nature | Springer International Publishing
    Publication Date: 2024-04-04
    Description: This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier–Stokes equations, and systems of nonlinear advection–diffusion–reaction equations, it guides readers through the essential steps to quickly solving a PDE in FEniCS, such as how to define a finite variational problem, how to set boundary conditions, how to solve linear and nonlinear systems, and how to visualize solutions and structure finite element Python programs. This book is open access under a CC BY license.
    Keywords: Computational Science and Engineering ; Algorithms ; Visualization ; Mathematical Software ; Numerical Analysis ; Software Engineering/Programming and Operating Systems ; Data and Information Visualization ; Software Engineering ; Finite element ; FEniCS ; Partial Differential Equations ; Python ; Simulation ; Open access ; Maths for scientists ; Combinatorics & graph theory ; Mathematical & statistical software ; Operating systems ; thema EDItEUR::P Mathematics and Science::PD Science: general issues::PDE Maths for scientists ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBV Combinatorics and graph theory ; thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software ; thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMZ Software Engineering
    Language: English
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 9
    Publication Date: 2024-04-04
    Description: Mathematics; Computer mathematics; Numerical analysis; Computer software; Numerical analysis
    Keywords: Mathematics ; Computer mathematics ; Numerical analysis ; Computer software ; Numerical analysis ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis ; thema EDItEUR::P Mathematics and Science::PD Science: general issues::PDE Maths for scientists ; thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation
    Language: English
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 10
    Publication Date: 2024-04-14
    Description: finite difference methods; programming; python; verification; numerical methods; differential equations
    Keywords: finite difference methods ; programming ; python ; verification ; numerical methods ; differential equations ; thema EDItEUR::U Computing and Information Technology::UY Computer science
    Language: English
    Format: image/jpeg
    Format: image/jpeg
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...