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