Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 9th International Particle Accelerator Conference
EditorsShane Koscielniak, Todd Satogata, Volker RW Schaa, Jana Thomson
Place of PublicationGeneva, Switzerland
PublisherJACoW
Pages3438-3440
Number of pages3
ISBN (Print)978-3-95450-184-7
DOIs
Publication statusPublished - Jun 2018

Fingerprint

Mathematical transformations
Engines
Neural networks
Program processors
Learning systems
Identification (control systems)
Polynomials
Graphics processing unit
Deep neural networks

Cite this

Ivanov, A., Andrianov, S., Kulabukhova, N., Sholokhova, A., Krushinevskii, E., & Sboeva, E. (2018). Matrix Representation of Lie Transform in TensorFlow. In S. Koscielniak, T. Satogata, V. RW. Schaa, & J. Thomson (Eds.), Proceedings of the 9th International Particle Accelerator Conference (pp. 3438-3440). Geneva, Switzerland: JACoW. https://doi.org/10.18429/JACoW-IPAC2018-THPAK088
Ivanov, A. ; Andrianov, S. ; Kulabukhova, N. ; Sholokhova, A. ; Krushinevskii, E. ; Sboeva, E. . / Matrix Representation of Lie Transform in TensorFlow. Proceedings of the 9th International Particle Accelerator Conference. editor / Shane Koscielniak ; Todd Satogata ; Volker RW Schaa ; Jana Thomson . Geneva, Switzerland : JACoW, 2018. pp. 3438-3440
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abstract = "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.",
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author = "A. Ivanov and S. Andrianov and N. Kulabukhova and A. Sholokhova and E. Krushinevskii and E. Sboeva",
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Ivanov, A, Andrianov, S, Kulabukhova, N, Sholokhova, A, Krushinevskii, E & Sboeva, E 2018, Matrix Representation of Lie Transform in TensorFlow. in S Koscielniak, T Satogata, VRW Schaa & J Thomson (eds), Proceedings of the 9th International Particle Accelerator Conference. JACoW, Geneva, Switzerland, pp. 3438-3440. https://doi.org/10.18429/JACoW-IPAC2018-THPAK088

Matrix Representation of Lie Transform in TensorFlow. / Ivanov, A.; Andrianov, S.; Kulabukhova, N.; Sholokhova, A.; Krushinevskii, E. ; Sboeva, E. .

Proceedings of the 9th International Particle Accelerator Conference. ed. / Shane Koscielniak; Todd Satogata; Volker RW Schaa; Jana Thomson . Geneva, Switzerland : JACoW, 2018. p. 3438-3440.

Research output

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T1 - Matrix Representation of Lie Transform in TensorFlow

AU - Ivanov, A.

AU - Andrianov, S.

AU - Kulabukhova, N.

AU - Sholokhova, A.

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AU - Sboeva, E.

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N2 - 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.

AB - 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.

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BT - Proceedings of the 9th International Particle Accelerator Conference

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Ivanov A, Andrianov S, Kulabukhova N, Sholokhova A, Krushinevskii E, Sboeva E. Matrix Representation of Lie Transform in TensorFlow. In Koscielniak S, Satogata T, Schaa VRW, Thomson J, editors, Proceedings of the 9th International Particle Accelerator Conference. Geneva, Switzerland: JACoW. 2018. p. 3438-3440 https://doi.org/10.18429/JACoW-IPAC2018-THPAK088