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PYU33C01 – Computer Simulation I

                                                                                                                                
Michaelmas Term – 30 lectures/tutorials – 5 credits (S Hutzler, S. Power)

Part I: Numerical Methods I
Part II: Computational Methods

Learning Outcomes
On successful completion of this module, students should be able to:

  • Apply standard numerical methods to problems of differentiation, integration and data handling
  • Compute numerical solutions to ordinary differential equations
  • Critically assess the application of Monte Carlo methods to minimization problems
  • Explain the concept of genetic algorithms

  • Use common Linux commands for file handling and data processing
  • Perform computational tasks in Python using both scripts and interactive notebooks
  • Import and call relevant functions from a variety of scientific computing libraries
  • Prepare publication quality figures from numerical data

Syllabus

Part I: Numerical Methods I
Numerical differentiation/integration, solving ordinary differential equations, random numbers, Monte Carlo methods, genetic algorithms and neural networks.

Part II: Computational Methods
Introduction to Linux: command line, shell scripts and data processing. Programming in Python: syntax, scripts, notebooks and data structures. Scientific libraries: importing external packages; common modules for arrays, numerics and plotting (Numpy, SciPy, Matplotlib). Applications to physical problems. Advanced topics.

Assessment

Weighting

Continuous Assessment in Numerical Methods 

50%

Continuous Assessment in Computational Methods

50%