A Rayleigh-Ritz Autoencoder

Publikationen: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Konferenzband

Standard

A Rayleigh-Ritz Autoencoder. / Terbuch, Anika; O'Leary, Paul; Ninevski, Dimitar et al.
2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023). Kuala Lumpur, Malaysia: Institute of Electrical and Electronics Engineers, 2023. S. 1-6.

Publikationen: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Konferenzband

Harvard

Terbuch, A, O'Leary, P, Ninevski, D, Hagendorfer, EJ, Schlager, E, Windisch, A & Schweimer, C 2023, A Rayleigh-Ritz Autoencoder. in 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023). Institute of Electrical and Electronics Engineers, Kuala Lumpur, Malaysia, S. 1-6, 2023 IEEE International Instrumentation and Measurement Technology
Conference (I2MTC) (I2MTC 2023), Kuala Lumpur, Malaysia, 22/05/23.

APA

Terbuch, A., O'Leary, P., Ninevski, D., Hagendorfer, E. J., Schlager, E., Windisch, A., & Schweimer, C. (2023). A Rayleigh-Ritz Autoencoder. In 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023) (S. 1-6). Institute of Electrical and Electronics Engineers.

Vancouver

Terbuch A, O'Leary P, Ninevski D, Hagendorfer EJ, Schlager E, Windisch A et al. A Rayleigh-Ritz Autoencoder. in 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023). Kuala Lumpur, Malaysia: Institute of Electrical and Electronics Engineers. 2023. S. 1-6

Author

Terbuch, Anika ; O'Leary, Paul ; Ninevski, Dimitar et al. / A Rayleigh-Ritz Autoencoder. 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023). Kuala Lumpur, Malaysia : Institute of Electrical and Electronics Engineers, 2023. S. 1-6

Bibtex - Download

@inproceedings{5d869d8009fa48e1b4d7facebc1d6e66,
title = "A Rayleigh-Ritz Autoencoder",
abstract = "This paper presents a new architecture for unsupervised hybrid machinelearning, called a Rayleigh Ritz Autoencoder (RRAE). It is suitable forapplications in instrumentation and measurement where the system beingobserved by multiple sensors is well modelled as a boundary value problem.The embedding of the admissible functions in the decoder implements a trulyphysics-informed machine learning architecture. The RRAE provides an exactfulfilment of Neumann, Cauchy, Dirichlet or periodic constraints. Only theencoder needs to be trained; consequently, the RRAE is numerically moreefficient during training than traditional autoencoders.The new Rayleigh-Ritz Autoencoder has been applied to an instrumentationand measurement problem in structural monitoring. It involves the fusionof data from multiple sensors and the solution of a boundary value problem.A 1-norm minimization has been chosen to minimize the effects ofnon-Gaussian perturbations and to demonstrate the non-linear abilities ofthe RRAE. The results from the tunnel monitoring application over months ofwork are presented in detail.{"}",
keywords = "Physics informed machine learning, Rayleigh-Ritz, Structural monitoring",
author = "Anika Terbuch and Paul O'Leary and Dimitar Ninevski and Hagendorfer, {Elias Jan} and Elke Schlager and Andreas Windisch and Christoph Schweimer",
year = "2023",
language = "English",
pages = "1--6",
booktitle = "2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023)",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",
note = "2023 IEEE International Instrumentation and Measurement Technology<br/>Conference (I2MTC) (I2MTC 2023) : nstrumentation and Measurement: Rising Above Covid-19, I2MTC 2023 ; Conference date: 22-05-2023 Through 25-05-2023",
url = "https://i2mtc2023.ieee-ims.org/",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - A Rayleigh-Ritz Autoencoder

AU - Terbuch, Anika

AU - O'Leary, Paul

AU - Ninevski, Dimitar

AU - Hagendorfer, Elias Jan

AU - Schlager, Elke

AU - Windisch, Andreas

AU - Schweimer, Christoph

PY - 2023

Y1 - 2023

N2 - This paper presents a new architecture for unsupervised hybrid machinelearning, called a Rayleigh Ritz Autoencoder (RRAE). It is suitable forapplications in instrumentation and measurement where the system beingobserved by multiple sensors is well modelled as a boundary value problem.The embedding of the admissible functions in the decoder implements a trulyphysics-informed machine learning architecture. The RRAE provides an exactfulfilment of Neumann, Cauchy, Dirichlet or periodic constraints. Only theencoder needs to be trained; consequently, the RRAE is numerically moreefficient during training than traditional autoencoders.The new Rayleigh-Ritz Autoencoder has been applied to an instrumentationand measurement problem in structural monitoring. It involves the fusionof data from multiple sensors and the solution of a boundary value problem.A 1-norm minimization has been chosen to minimize the effects ofnon-Gaussian perturbations and to demonstrate the non-linear abilities ofthe RRAE. The results from the tunnel monitoring application over months ofwork are presented in detail."

AB - This paper presents a new architecture for unsupervised hybrid machinelearning, called a Rayleigh Ritz Autoencoder (RRAE). It is suitable forapplications in instrumentation and measurement where the system beingobserved by multiple sensors is well modelled as a boundary value problem.The embedding of the admissible functions in the decoder implements a trulyphysics-informed machine learning architecture. The RRAE provides an exactfulfilment of Neumann, Cauchy, Dirichlet or periodic constraints. Only theencoder needs to be trained; consequently, the RRAE is numerically moreefficient during training than traditional autoencoders.The new Rayleigh-Ritz Autoencoder has been applied to an instrumentationand measurement problem in structural monitoring. It involves the fusionof data from multiple sensors and the solution of a boundary value problem.A 1-norm minimization has been chosen to minimize the effects ofnon-Gaussian perturbations and to demonstrate the non-linear abilities ofthe RRAE. The results from the tunnel monitoring application over months ofwork are presented in detail."

KW - Physics informed machine learning

KW - Rayleigh-Ritz

KW - Structural monitoring

M3 - Conference contribution

SP - 1

EP - 6

BT - 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023)

PB - Institute of Electrical and Electronics Engineers

CY - Kuala Lumpur, Malaysia

T2 - 2023 IEEE International Instrumentation and Measurement Technology<br/>Conference (I2MTC) (I2MTC 2023)

Y2 - 22 May 2023 through 25 May 2023

ER -