Part II Computational Physics
Part II Computational Physics
Notes
Course outline
Introduction
Getting going
NumPy and friends
Floating point and all that
Solving differential equations with SciPy
Monte Carlo methods
Algorithms and computational complexity
Fast Fourier transform
Automatic differentiation and neural networks
Linear algebra
Slides
Getting going
NumPy and friends
Floating point and ODEs
Monte Carlo methods
Algorithms and computational complexity
Fast Fourier transform
Autodiff and neural nets
Linear algebra
Exercises and Projects
Exercises
Projects
On this page
1
Assignments
1.1
1. PDEs
1.2
2. Linear Algebra and Quantum
1.3
3. Dynamic Programming
1.4
4. Ising model
1
Assignments
1.1
1. PDEs
1.2
2. Linear Algebra and Quantum
1.3
3. Dynamic Programming
1.4
4. Ising model
Autocorrelation times of various algorithms. Scaling with system size.