Learn Physics with Functional Programming (No Starch Press)
Friday, 17 February 2023

This book sets out to unlock the mysteries of theoretical physics by coding the underlying math in Haskell. Scott Walck shows how to use Haskell’s type system to check that your code makes sense. Walck explains Newtonian mechanics and electromagnetic theory, including how to describe and calculate electric and magnetic fields.

<ASIN:1718501668>

 

Author: Scott Walck
Publisher: No Starch Press
Date: January 2023
Pages: 648
ISBN: 978-1718501669
Print:1718501668
Kindle: B09WJWSFW8
Audience: People interested in physics
Level: Intermediate/Advanced
Category: Other Languages and Mathematics

physicshaskell

Topics include:

 

  • Encode vectors, derivatives, integrals, scalar fields, vector fields, and differential equations
  • Express fundamental physical principles using the logic of Haskell’s type system to clarify Newton’s second law, Coulomb’s law, the Biot-Savart law, and the Maxwell equations
  • Use higher-order functions to express numerical integration and approximation methods, such as the Euler method and the finite-difference time-domain (FDTD) method
  • Create graphs, models, and animations of physical scenarios like colliding billiard balls, waves in a guitar string, and a proton in a magnetic field

 

For recommendations of functional programming books see First Class Functional Programming Books in our Programmer's Bookshelf section.

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Computer Graphics from Scratch (No Starch Press)

Author: Gabriel Gambetta
Publisher: No Starch Press
Pages: 248
ISBN: 978-1718500761
Print: 1718500769
Kindle: B085BVJG5B
Audience: People interested in creating 3D graphics
Rating: 5
Reviewer: Kay Ewbank

This is a well written book that explains the topic of 3D rendering, and tries its hardest to mi [ ... ]



TinyML: Machine Learning with TensorFlow Lite

Authors: Pete Warden and Daniel Situnayake
Publisher: O'Reilly
Date: December 2019
Pages: 504
ISBN: 978-1492052043
Print: 1492052043
Kindle: B082TY3SX7
Audience: Developers interested in machine learning
Rating: 5, but see reservations
Reviewer: Harry Fairhead
Can such small machines really do ML?


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