I took Mark Newman’s course some years ago. It was fantastic! Geared at sophomore/ junior year physics major — someone who had completed the basic intro sequence. I am sure this book is also great.
vectorcrumb 3 hours ago [-]
Could somebody provide some opinion on the book and/or accompanying course?
HexDecOctBin 2 hours ago [-]
What physics do I need to know to follow this book?
griffzhowl 2 hours ago [-]
Looks like not much. The book is about using Python to implement numerical methods, mainly about teaching the Python part, and that's all explained. You might be missing motivation if you don't know any physics, but even so, basic mechanics using differential equations seems to be enough to give context, at least for the earlier parts
mapt 2 hours ago [-]
> Exercises by chapter
Click on a chapter to download:
Chapter 2: Python programming for physicists
Chapter 3: Graphics and visualization
Chapter 4: Accuracy and speed
Chapter 5: Integrals and derivatives
Chapter 6: Solution of linear and nonlinear equations
Chapter 7: Fourier transforms
Chapter 8: Ordinary differential equations
Chapter 9: Partial differential equations
Chapter 10: Random processes and Monte Carlo methods
Chapter 11: Data science
ktallett 2 hours ago [-]
I did a few courses across academic years that were based around this book and it's very handy skills to learn. Whilst perhaps not in the moment, it's a good introduction to implementing functions and equations, before you lead on to the next steps of specific functions and methods of analysis alongside hpc with parallelization.
ninjahawk1 1 hours ago [-]
good book
Rendered at 20:05:40 GMT+0000 (Coordinated Universal Time) with Vercel.
Click on a chapter to download:
Chapter 2: Python programming for physicists
Chapter 3: Graphics and visualization
Chapter 4: Accuracy and speed
Chapter 5: Integrals and derivatives
Chapter 6: Solution of linear and nonlinear equations
Chapter 7: Fourier transforms
Chapter 8: Ordinary differential equations
Chapter 9: Partial differential equations
Chapter 10: Random processes and Monte Carlo methods
Chapter 11: Data science