Abstract
We present auto_diff, a package that performs automatic differentiation of numerical Python code. auto_diff overrides Python’s NumPy package’s functions, augmenting them with seamless automatic differentiation capabilities. Notably, auto_diff is non-intrusive, i.e., the code to be differentiated does not require auto_diff-specific alterations. We illustrate auto_diff on electronic devices, a circuit simulation, and a mechanical system simulation. In our evaluations so far, we found that running simulations with auto_diff takes less than 4 times as long as simulations with hand-written differentiation code. We believe that auto_diff, which was written after attempts to use existing automatic differentiation packages on our applications ran into difficulties, caters to an important need within the numerical Python community. We have attempted to write this paper in a tutorial style to make it accessible to those without prior background in automatic differentiation techniques and packages. We have released auto_diff as open source on GitHub.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
