Learning Trajectories of Hamiltonian Systems with Neural Networks

Katsiaryna Haitsiukevich; Alexander Ilin

doi:10.1007/978-3-031-15919-0_47

Learning Trajectories of Hamiltonian Systems with Neural Networks

Katsiaryna Haitsiukevich, Alexander Ilin

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

1 Sitaatiot (Scopus)

35 Lataukset (Pure)

Abstrakti

Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's equations of motion. Many recent works focus on improving the integration schemes used when training HNNs. In this work, we propose to enhance HNNs with an estimation of a continuous-time trajectory of the modeled system using an additional neural network, called a deep hidden physics model in the literature. We demonstrate that the proposed integration scheme works well for HNNs, especially with low sampling rates, noisy and irregular observations.

Alkuperäiskieli	Englanti
Otsikko	Artificial Neural Networks and Machine Learning – ICANN 2022
Toimittajat	Elias Pimenidis, Mehmet Aydin, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas
Julkaisupaikka	Bristol, UK
Kustantaja	Springer
Sivut	562–573
Sivumäärä	12
ISBN (elektroninen)	978-3-031-15919-0
ISBN (painettu)	978-3-031-15918-3
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-031-15919-0_47
Tila	Julkaistu - 7 syysk. 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Artificial Neural Networks - University of the West of England (UWE Bristol), Bristol, Iso-Britannia Kesto: 6 syysk. 2022 → 9 syysk. 2022 https://e-nns.org/icann2022/

Julkaisusarja

Nimi	Lecture Notes in Computer Science
Kustantaja	Springer
Vuosikerta	13529
ISSN (painettu)	0302-9743

Conference

Conference	International Conference on Artificial Neural Networks
Lyhennettä	ICANN
Maa/Alue	Iso-Britannia
Kaupunki	Bristol
Ajanjakso	06/09/2022 → 09/09/2022
www-osoite	https://e-nns.org/icann2022/

Pääsy asiakirjaan

10.1007/978-3-031-15919-0_47

SCI_Haitsiukevich_etal_ICANN_2022Accepted author manuscript, 379 KB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

-: Finnish Center for Artificial Intelligence
Kaski, S. (Vastuullinen tutkija)
01/01/2019 → 31/12/2022
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

Haitsiukevich, K., & Ilin, A. (2022). Learning Trajectories of Hamiltonian Systems with Neural Networks. teoksessa E. Pimenidis, M. Aydin, P. Angelov, C. Jayne, & A. Papaleonidas (Toimittajat), Artificial Neural Networks and Machine Learning – ICANN 2022 (Sivut 562–573). (Lecture Notes in Computer Science; Vuosikerta 13529). Springer. https://doi.org/10.1007/978-3-031-15919-0_47

@inproceedings{023a64af7b274b9b847f866df83ac030,

title = "Learning Trajectories of Hamiltonian Systems with Neural Networks",

abstract = "Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's equations of motion. Many recent works focus on improving the integration schemes used when training HNNs. In this work, we propose to enhance HNNs with an estimation of a continuous-time trajectory of the modeled system using an additional neural network, called a deep hidden physics model in the literature. We demonstrate that the proposed integration scheme works well for HNNs, especially with low sampling rates, noisy and irregular observations.",

keywords = "Conservative systems, Deep Hidden Physics Models, Dynamical systems, Hamiltonian Neural Networks, Physics-Informed Neural Networks",

author = "Katsiaryna Haitsiukevich and Alexander Ilin",

year = "2022",

month = sep,

day = "7",

doi = "10.1007/978-3-031-15919-0_47",

language = "English",

isbn = "978-3-031-15918-3",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "562–573",

editor = "Elias Pimenidis and Mehmet Aydin and Plamen Angelov and Chrisina Jayne and Antonios Papaleonidas",

booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2022",

address = "Germany",

note = "International Conference on Artificial Neural Networks, ICANN ; Conference date: 06-09-2022 Through 09-09-2022",

url = "https://e-nns.org/icann2022/",

}

Haitsiukevich, K & Ilin, A 2022, Learning Trajectories of Hamiltonian Systems with Neural Networks. julkaisussa E Pimenidis, M Aydin, P Angelov, C Jayne & A Papaleonidas (toim), Artificial Neural Networks and Machine Learning – ICANN 2022. Lecture Notes in Computer Science, Vuosikerta 13529, Springer, Bristol, UK, Sivut 562–573, International Conference on Artificial Neural Networks, Bristol, Iso-Britannia, 06/09/2022. https://doi.org/10.1007/978-3-031-15919-0_47

Learning Trajectories of Hamiltonian Systems with Neural Networks. / Haitsiukevich, Katsiaryna ; Ilin, Alexander.
Artificial Neural Networks and Machine Learning – ICANN 2022. toim. / Elias Pimenidis; Mehmet Aydin; Plamen Angelov; Chrisina Jayne; Antonios Papaleonidas. Bristol, UK: Springer, 2022. s. 562–573 (Lecture Notes in Computer Science; Vuosikerta 13529).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Learning Trajectories of Hamiltonian Systems with Neural Networks

AU - Haitsiukevich, Katsiaryna

AU - Ilin, Alexander

PY - 2022/9/7

Y1 - 2022/9/7

N2 - Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's equations of motion. Many recent works focus on improving the integration schemes used when training HNNs. In this work, we propose to enhance HNNs with an estimation of a continuous-time trajectory of the modeled system using an additional neural network, called a deep hidden physics model in the literature. We demonstrate that the proposed integration scheme works well for HNNs, especially with low sampling rates, noisy and irregular observations.

AB - Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's equations of motion. Many recent works focus on improving the integration schemes used when training HNNs. In this work, we propose to enhance HNNs with an estimation of a continuous-time trajectory of the modeled system using an additional neural network, called a deep hidden physics model in the literature. We demonstrate that the proposed integration scheme works well for HNNs, especially with low sampling rates, noisy and irregular observations.

KW - Conservative systems

KW - Deep Hidden Physics Models

KW - Dynamical systems

KW - Hamiltonian Neural Networks

KW - Physics-Informed Neural Networks

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U2 - 10.1007/978-3-031-15919-0_47

DO - 10.1007/978-3-031-15919-0_47

M3 - Conference article in proceedings

SN - 978-3-031-15918-3

T3 - Lecture Notes in Computer Science

SP - 562

EP - 573

BT - Artificial Neural Networks and Machine Learning – ICANN 2022

A2 - Pimenidis, Elias

A2 - Aydin, Mehmet

A2 - Angelov, Plamen

A2 - Jayne, Chrisina

A2 - Papaleonidas, Antonios

PB - Springer

CY - Bristol, UK

T2 - International Conference on Artificial Neural Networks

Y2 - 6 September 2022 through 9 September 2022

ER -

Learning Trajectories of Hamiltonian Systems with Neural Networks

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

-: Finnish Center for Artificial Intelligence

Laitteet

Science-IT

Siteeraa tätä