Algorithmic differentiation (AD) by source-transformation is an established method for computing derivatives of computational algorithms.
Dec 9, 2016 · Algorithmic differentiation (AD) by source-transformation is an established method for computing derivatives of com- putational algorithms.
Algorithmic differentiation (AD) by source-transformation is an established method for computing derivatives of com- putational algorithms.
Algorithmic differentiation (AD) by source-transformation is an established method for computing derivatives of computational algorithms.
This is a pre-copyedited, author-produced PDF of an article accepted for publication in ACM Trans. Math. Softw. following peer review.
Algorithmic Differentiation (AD), also known as Automatic. Differentiation, is a technology for accurate and efficient evaluation of derivatives of a ...
Algorithmic differentiation (AD) by source-transformation is an established method for computing derivatives of computational algorithms. Static dataflow ...
Algorithmic differentiation of code with multiple context-specific activities. JC Hueckelheim, L Hascoët, JD Müller. ACM Transactions on Mathematical Software ...
Automatic differentiation (AD) has been expanding its role in scientific computing. While several AD tools have been actively developed and used, ...
Missing: Multiple Specific
A computational fluid dynamics code is differentiated using algorithmic differentiation (AD) in both tangent and adjoint modes.