Description

An example-motivated, visual introductory numerical computation course utilizing Python and its scientific libraries: NumPy, SciPy, Matplotlib. Topics include interpolation and data analysis, ordinary and partial differential equations, linear algebra and eigenvalue problems, discrete and fast Fourier transform methods. Physics applications emphasized.

Completed assignments

• Lorenz attractor (ODE): Ref. 1

Potential assignments

• Stock market forecasting (interpolation)
• Cursive writing (spline interpolation)
• Newton's fractal (Root finding) => Mandelbrot fractal: Ref. 1
• Double pendulum (ODE)
• Solar system (ODE)
• Heat diffusion (PDE)
• Drum simulation - wave equation (PDE)
• Earthquake simulation (PDE)
• JPEG compression (FFT)
• Remove audio noise (FFT)
• Message decoding (FFT)
• Global warming (data analysis/FFT)
• Performance comparison (DFT/FFT, Newton vs. Secant methods)
• Google PageRank (linear algebra)
• 3D transformations (linear algebra): Ref. 1