This page is a list of what I think are useful resources for people doing computational physics, with a bias towards accelerator physics.
Books:
Numerical Methods:
Computational Physics and Scientific Computing – the best book I’ve seen so far
Geometric Numerical Integration
Computational Physics with Python:
http://www.composingprograms.com/
Computational Physics with Python
Learning SciPy for Numerical and Scientific Computing
A Primer on Scientific Programming with Python
Computational Physics Notes:
- http://mathfaculty.fullerton.edu/mathews//numerical.html
- http://www.physics.udel.edu/~bnikolic/teaching/phys660/numerical_ode/node5.html
- faculty.olin.edu/bstorey/Notes/DiffEq.pdf
- http://www.math.unl.edu/~gledder1/Math447/EulerError
- http://young.physics.ucsc.edu/115/
- A molecular dynamics primer
- Time-corrected Verlet
- http://mathfaculty.fullerton.edu/mathews//n2003/NumericalUndergradMod.html
- Angus Mackinnon’s notes
Monte Carlo and its Validation
Here I list some links I am collecting on the validation of Monte Carlo tools, mainly that use GEANT4 underneath them.
- PTSIM vs gminos
- Experimental validation of the TOPAS Monte Carlo system for passive scattering proton therapy
- Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4