Difference between revisions of "Isc3313 schedule"

 
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([[ISC-3313 | Overview]] | [[isc3313_syllabus | Syllabus]] | [[isc3313_schedule | Schedule]] | [[isc3313_lectures | Lectures]] |  [[isc3313_assign | Assignments]] | [[isc3313_project | Project]] | [[isc3313_misc | Misc]])
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([[ISC-3313 | Overview]] | [[isc3313_syllabus | Syllabus]] | [[isc3313_schedule | Schedule]] | [[isc3313_lectures | Lectures]] |  [[isc3313_assign | Assignments]] | [[isc3313_project | Project]] | [http://www.peterbeerli.com/classdata/ISC3313/codes Code] | [[isc3313_misc | Misc]])
  
I. January 6, 2014 Components of Scientific Computing<br>
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I. Components of Scientific Computing<br>
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1. Syllabus and introduction<br>
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2. UNIX basics<br>
  
 
II. A simple example - Using a Monte Carlo approach to approximate problems<br>
 
II. A simple example - Using a Monte Carlo approach to approximate problems<br>
1. January 8 2014 UNIX basics<br>
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1. Working C++ programming projects<br>
2. January 10 2014 Netbeans IDE: an integrated development environment for C++ programming<br>
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2. Introduction to C++<br>
3. January 10 2014 Introduction to C++<br>
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3. Algorithm development (Monte Carlo Integration)<br>
4. January 13/15 2014 Algorithm development (Monte Carlo Integration)<br>
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4. Program testing and documentation<br>
5. January 15/17/22 2014 Program testing and documentation<br>
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5. Visualization and analysis of results<br>
6. January 24 Visualization and analysis of results<br>
 
  
III. Solving a non-linear equations<br>
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III. Solving non-linear equations<br>
1. January 27 Description of problem and some simple algorithms<br>
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1. Description of problem and some simple algorithms<br>
2. (January 27 Iterative methods, required accuracy of result)<br>
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2.Iterative methods, required accuracy of result<br>
3. January 29 - February 7 Implementation of the Bisection method<br>
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3. Implementation of the Bisection method<br>
4. (Program testing and documentation)<br>
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4. Program testing and documentation<br>
  
 
IV.Object oriented programming concepts in detail<br>
 
IV.Object oriented programming concepts in detail<br>
 
using the non-linear equation problem and implementing more methods<br>
 
using the non-linear equation problem and implementing more methods<br>
1. February  10 Encapsulation<br>
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1. Encapsulation<br>
2. February  10-21 Inheritance<br>
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2.Inheritance<br>
3. (February  10-21 Polymorphism)<br>
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3. Polymorphism<br>
4. (not done yet: Abstract classes and datatypes)<br>
 
  
V. Operations on vectors and matrices<br>
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V. Operations on vectors and matrices (February)<br>
1. February 24: Development of general functionality that is usable in many places (a class 'Rational')<br>
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1. Development of general functionality that is usable in many places (a class 'Rational')<br>
 
2. Vector and Matrix operations<br>
 
2. Vector and Matrix operations<br>
 
3. Vector norms<br>
 
3. Vector norms<br>
4. Concurrency and parallel processing of such calculations using C++<br>
 
  
VI. Polynomial interpolation of data<br>
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VI. Polynomial interpolation of data (March) <br>
 
1. Description of problems and (biological) applications<br>
 
1. Description of problems and (biological) applications<br>
2. Algorithms: Lagrangian interpolation in detail<br>
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2. Algorithms: Lagrangian and Newton interpolation in detail<br>
 
3. Implementation to fit a set of data<br>
 
3. Implementation to fit a set of data<br>
 
4. Piecewise interpolation<br>
 
4. Piecewise interpolation<br>
5. Implementation and visualization of of piecewise interpolation<br>
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5. Implementation and visualization of piecewise interpolation<br>
  
VII.Solving ordinary differential equations systems<br>
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VII.Solving ordinary differential equations systems (April)<br>
1.Description of problem: Lotka-Volterra Predator-Prey system<br>
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1. Simple ODE solved using Euler's method
2.Algorithms<br>
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2. ODE solved using Runge-Kutta method
3.How to use functions from other libraries<br>
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3. Multipoint methods
4.How to assess correctness of program<br>
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4. Description of a two-equation system: Lotka-Volterra Predator-Prey system<br>
5.Visualization of results<br>
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5. Algorithms and implementation<br>
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6. Visualization of results<br>
  
 
VIII. Markov chain Monte Carlo Integration<br>
 
VIII. Markov chain Monte Carlo Integration<br>
1.Description of method<br>
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1. Description of method<br>
2.Example application<br>
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2. Example application<br>
3.Implementation <br>
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3. Implementation <br>
4.Testing and visualization of results<br>
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4. Testing and visualization of results<br>
  
IX.Capstone project<br>
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IX. Capstone project<br>

Latest revision as of 14:08, 29 August 2016

( Overview | Syllabus | Schedule | Lectures | Assignments | Project | Code | Misc)

I. Components of Scientific Computing
1. Syllabus and introduction
2. UNIX basics

II. A simple example - Using a Monte Carlo approach to approximate problems
1. Working C++ programming projects
2. Introduction to C++
3. Algorithm development (Monte Carlo Integration)
4. Program testing and documentation
5. Visualization and analysis of results

III. Solving non-linear equations
1. Description of problem and some simple algorithms
2.Iterative methods, required accuracy of result
3. Implementation of the Bisection method
4. Program testing and documentation

IV.Object oriented programming concepts in detail
using the non-linear equation problem and implementing more methods
1. Encapsulation
2.Inheritance
3. Polymorphism

V. Operations on vectors and matrices (February)
1. Development of general functionality that is usable in many places (a class 'Rational')
2. Vector and Matrix operations
3. Vector norms

VI. Polynomial interpolation of data (March)
1. Description of problems and (biological) applications
2. Algorithms: Lagrangian and Newton interpolation in detail
3. Implementation to fit a set of data
4. Piecewise interpolation
5. Implementation and visualization of piecewise interpolation

VII.Solving ordinary differential equations systems (April)
1. Simple ODE solved using Euler's method 2. ODE solved using Runge-Kutta method 3. Multipoint methods 4. Description of a two-equation system: Lotka-Volterra Predator-Prey system
5. Algorithms and implementation
6. Visualization of results

VIII. Markov chain Monte Carlo Integration
1. Description of method
2. Example application
3. Implementation
4. Testing and visualization of results

IX. Capstone project