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.library.json
.library.json
 
 
02_Dimensional_Analysis.ipynb
02_Dimensional_Analysis.ipynb
 
 
03_Power_Series.ipynb
03_Power_Series.ipynb
 
 
04_Spherical_and_Cylindrical_Coordinates.ipynb
04_Spherical_and_Cylindrical_Coordinates.ipynb
 
 
05_Gradient.ipynb
05_Gradient.ipynb
 
 
06_Divergence.ipynb
06_Divergence.ipynb
 
 
07_Curl.ipynb
07_Curl.ipynb
 
 
08_Gauss.ipynb
08_Gauss.ipynb
 
 
09_Stokes.ipynb
09_Stokes.ipynb
 
 
10_Laplacian.ipynb
10_Laplacian.ipynb
 
 
11_Scale_Analysis.ipynb
11_Scale_Analysis.ipynb
 
 
12_Linear_Algebra.ipynb
12_Linear_Algebra.ipynb
 
 
13_Dirac_Delta_Function.ipynb
13_Dirac_Delta_Function.ipynb
 
 
14_Fourier_Analysis.ipynb
14_Fourier_Analysis.ipynb
 
 
15_Analytic_Functions.ipynb
15_Analytic_Functions.ipynb
 
 
16_Complex_Integration.ipynb
16_Complex_Integration.ipynb
 
 
17_Greens_Functions.ipynb
17_Greens_Functions.ipynb
 
 
18_GF_examples.ipynb
18_GF_examples.ipynb
 
 
19_Normal_Modes.ipynb
19_Normal_Modes.ipynb
 
 
20_Potential_Methods.ipynb
20_Potential_Methods.ipynb
 
 
21_Probability_and_Statistics.ipynb
21_Probability_and_Statistics.ipynb
 
 
22_Inverse_Theory.ipynb
22_Inverse_Theory.ipynb
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
environment.yml
environment.yml
 
 

README.md

mathematical-notebooks-for-the-physical-sciences

Notebooks to accompany our book "A guided Tour of Mathematical Methods for the Physical Sciences"

A Guided Tour of Mathematical Methods for the Physical Sciences is based on a philosophy that learning mathematics for the physical sciences, requires pen and paper. Learning by doing. Three editions in, this philosophy still holds strong, but heaps of things have happened since the first edition in 2001. Numerical methods play an ever-larger role in the physical sciences, for example. The Python programming language has really exploded on the scene, and more recently the jupyter notebook has been developed to facilitate learning numerical methods in python. To accompany the latest edition of our book - and your pen and paper - we wrote one jupyter notebook per chapter.

If you are new to jupyter notebooks and/or python, the online resources are virtually endless, and the installation -- on any operating system -- is easy with conda. We are going to assume you run python 3.

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