Advances in Automatic. Differentiation for the Java. Programming Language - Emil-Ioan Slușanschi
Derivatives are a crucial component in many areas of science and engineering, and their accurate evaluation is often required in various scientific applications.
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Data disponibilității: 2015-03-31
Derivatives are a crucial component in many areas of science and engineering, and their accurate evaluation is often required in various scientific applications. The fact that to date no useable Automatic Differentiation tool implementation exists for Java motivated the development of an Automatic Differentiation tool for the Java language. The ADiJaC (Automatic Differentiation for Java Class files) tool implements these transformations in both the forward and reverse mode of automatic differentiation.
Emil-Ioan Sluşanschi is an associate professor at the University Politehnica of Bucharest. He received his MSc in Computer Science from the UPB in 2001 and his PhD from the Institute for Scientific Computing at the RWTH-Aachen University in Germany in 2008. His main fields of interest include HPC Applications, Parallel and Distributed System Architectures and Algorithms, Automatic Differentiation, and Wireless Sensor Networks. He has acquired significant experience in international research projects, as coordinator of the UPB management and research teams in the FP7 EUWB, TWISNet, and LEXNET projects. He is also a member of the following national and international professional associations: ACM, ACM-SIGHPC, IEEE, SIAM, and founding member of Asociatia Romana pentru promovarea Metodelor Computationale Avansate in Cercetarea Stiintifica – ARCAS, and Asociatia Automatica si Calculatoare – AAC.
|Format||140 x 200 mm|
|Colecția||Studii şi eseuri|
|ID Hard Cover||1406-advances-in-automatic-differentiation-for-the-java-programming-language-emil-ioan-sluanschi-9789735969080.html|
|Sub-Categorie||Stiinta si tehnica|