This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computa
Automatic Differentiation: Applications, Theory, and Implementations (Lecture Notes in Computational Science and Engineering)
β Scribed by Martin Bucker, George Corliss, Paul Hovland, Uwe Naumann, Boyana Norris (Editors)
- Year
- 2006
- Tongue
- English
- Leaves
- 370
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This collection covers the state of the art in automatic differentiation theory and practice. Practitioners and students will learn about advances in automatic differentiation techniques and strategies for the implementation of robust and powerful tools. Computational scientists and engineers will benefit from the discussion of applications, which provide insight into effective strategies for using automatic differentiation for design optimization, sensitivity analysis, and uncertainty quantification.
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