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Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. / Ivanov, Andrei ; Golovkina, Anna ; Iben, Uwe.

ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. ed. / Giuseppe De Giacomo; Alejandro Catala; Bistra Dilkina; Michela Milano; Senen Barro; Alberto Bugarin; Jerome Lang. IOS Press, 2020. p. 1230-1237 (Frontiers in Artificial Intelligence and Applications; Vol. 325).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Ivanov, A, Golovkina, A & Iben, U 2020, Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. in G De Giacomo, A Catala, B Dilkina, M Milano, S Barro, A Bugarin & J Lang (eds), ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 325, IOS Press, pp. 1230-1237, 24th European Conference on Artificial Intelligence, Сантьяго де Компостела, Spain, 29/08/20. https://doi.org/10.3233/FAIA200223

APA

Ivanov, A., Golovkina, A., & Iben, U. (2020). Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. In G. De Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Eds.), ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings (pp. 1230-1237). (Frontiers in Artificial Intelligence and Applications; Vol. 325). IOS Press. https://doi.org/10.3233/FAIA200223

Vancouver

Ivanov A, Golovkina A, Iben U. Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. In De Giacomo G, Catala A, Dilkina B, Milano M, Barro S, Bugarin A, Lang J, editors, ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. IOS Press. 2020. p. 1230-1237. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/FAIA200223

Author

Ivanov, Andrei ; Golovkina, Anna ; Iben, Uwe. / Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. editor / Giuseppe De Giacomo ; Alejandro Catala ; Bistra Dilkina ; Michela Milano ; Senen Barro ; Alberto Bugarin ; Jerome Lang. IOS Press, 2020. pp. 1230-1237 (Frontiers in Artificial Intelligence and Applications).

BibTeX

@inproceedings{8604042ba5174ca39cf4b1ace75472b8,
title = "Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning",
abstract = "The paper discusses the connection of Taylor maps and polynomial neural networks (PNN) for numerical solving of the ordinary differential equations (ODEs). Having the system of ODEs, it is possible to calculate weights of PNN that simulates the dynamics of these equations. It is shown that proposed PNN architecture can provide better accuracy with less computational time in comparison with traditional numerical solvers. Moreover, neural network derived from the ODEs can be used for simulation of system dynamics with different initial conditions, but without training procedure. Besides, if the equations are unknown, the weights of the PNN can be fitted in a data-driven way. In the paper, we describe the connection of PNN with differential equations theoretically along with the examples for both dynamics simulation and learning with data.",
author = "Andrei Ivanov and Anna Golovkina and Uwe Iben",
year = "2020",
month = aug,
day = "24",
doi = "10.3233/FAIA200223",
language = "English",
isbn = "978-1-64368-100-9",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "1230--1237",
editor = "{De Giacomo}, Giuseppe and Alejandro Catala and Bistra Dilkina and Michela Milano and Senen Barro and Alberto Bugarin and Jerome Lang",
booktitle = "ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings",
address = "Netherlands",
note = "null ; Conference date: 29-08-2020 Through 08-09-2020",

}

RIS

TY - GEN

T1 - Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning

AU - Ivanov, Andrei

AU - Golovkina, Anna

AU - Iben, Uwe

N1 - Conference code: 24

PY - 2020/8/24

Y1 - 2020/8/24

N2 - The paper discusses the connection of Taylor maps and polynomial neural networks (PNN) for numerical solving of the ordinary differential equations (ODEs). Having the system of ODEs, it is possible to calculate weights of PNN that simulates the dynamics of these equations. It is shown that proposed PNN architecture can provide better accuracy with less computational time in comparison with traditional numerical solvers. Moreover, neural network derived from the ODEs can be used for simulation of system dynamics with different initial conditions, but without training procedure. Besides, if the equations are unknown, the weights of the PNN can be fitted in a data-driven way. In the paper, we describe the connection of PNN with differential equations theoretically along with the examples for both dynamics simulation and learning with data.

AB - The paper discusses the connection of Taylor maps and polynomial neural networks (PNN) for numerical solving of the ordinary differential equations (ODEs). Having the system of ODEs, it is possible to calculate weights of PNN that simulates the dynamics of these equations. It is shown that proposed PNN architecture can provide better accuracy with less computational time in comparison with traditional numerical solvers. Moreover, neural network derived from the ODEs can be used for simulation of system dynamics with different initial conditions, but without training procedure. Besides, if the equations are unknown, the weights of the PNN can be fitted in a data-driven way. In the paper, we describe the connection of PNN with differential equations theoretically along with the examples for both dynamics simulation and learning with data.

UR - http://ebooks.iospress.nl/volume/ecai-2020-24th-european-conference-on-artificial-intelligence

UR - http://www.scopus.com/inward/record.url?scp=85091775761&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/829849c1-408a-31f3-b62b-c16814fb67bd/

U2 - 10.3233/FAIA200223

DO - 10.3233/FAIA200223

M3 - Conference contribution

SN - 978-1-64368-100-9

T3 - Frontiers in Artificial Intelligence and Applications

SP - 1230

EP - 1237

BT - ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings

A2 - De Giacomo, Giuseppe

A2 - Catala, Alejandro

A2 - Dilkina, Bistra

A2 - Milano, Michela

A2 - Barro, Senen

A2 - Bugarin, Alberto

A2 - Lang, Jerome

PB - IOS Press

Y2 - 29 August 2020 through 8 September 2020

ER -

ID: 62094035