IPAC2018 - List of Authors (Sholokhova, A.A.)

Paper	Title	Page
THPAK088	Matrix Representation of Lie Transform in TensorFlow	3438
	A.N. Ivanov, S.N. Andrianov, N.V. Kulabukhova, A.A. Sholokhova St. Petersburg State University, St. Petersburg, Russia E. Krushinevskii, E. Sboeva Saint Petersburg State University, Saint Petersburg, Russia
	In the article, we propose an implementation of the matrix representation of Lie transform using TensorFlow as a computational engine. TensorFlow allows easy description of deep neural networks and provides automatic code execution on both single CPU/GPU and cluster architectures. In this research, we demonstrate the connection of the matrix Lie transform with polynomial neural networks. The architecture of the neural network is described and realized in code. In terms of beam dynamics, the proposed technique provides a tool for both simulation and analysis of experimental results using modern machine learning techniques. As a simulation technique one operates with a nonlinear map up to the necessary order of nonlinearity. On the other hand, one can utilize TensorFlow engine to run map optimization and system identification problems.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THPAK088
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Paper

Title

Page

Matrix Representation of Lie Transform in TensorFlow

3438

A.N. Ivanov, S.N. Andrianov, N.V. Kulabukhova, A.A. Sholokhova
St. Petersburg State University, St. Petersburg, Russia
E. Krushinevskii, E. Sboeva
Saint Petersburg State University, Saint Petersburg, Russia

In the article, we propose an implementation of the matrix representation of Lie transform using TensorFlow as a computational engine. TensorFlow allows easy description of deep neural networks and provides automatic code execution on both single CPU/GPU and cluster architectures. In this research, we demonstrate the connection of the matrix Lie transform with polynomial neural networks. The architecture of the neural network is described and realized in code. In terms of beam dynamics, the proposed technique provides a tool for both simulation and analysis of experimental results using modern machine learning techniques. As a simulation technique one operates with a nonlinear map up to the necessary order of nonlinearity. On the other hand, one can utilize TensorFlow engine to run map optimization and system identification problems.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THPAK088

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)