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5 - Automatic Differentiation

from Part II - Practical Tools

Published online by Cambridge University Press:  22 September 2022

Seon Ki Park
Affiliation:
Ewha Womans University, Republic of Korea
Milija Zupanski
Affiliation:
Colorado State University
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Summary

Generating the adjoint model (ADJM) by hand is tedious, time-consuming, and error prone. In most practical applications of data assimilation these days, the derivative codes, including the ADJM, are generated by the automatic differentiation (AD) tools, which evaluate the exact derivative information of a function in terms of a program. Terminologies and methods in AD are introduced, including the practical exclusion of the forward and reverse modes of differentiation. Various AD tools based on two major AD approaches, source transformation and operator overloading, are compiled with their webpages.

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Publisher: Cambridge University Press
Print publication year: 2022

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