A Rayleigh-Ritz Autoencoder

Anika Terbuch, Paul O'Leary, Dimitar Ninevski, Elias Jan Hagendorfer, Elke Schlager, Andreas Windisch, Christoph Schweimer

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Konferenzband


This paper presents a new architecture for unsupervised hybrid machine
learning, called a Rayleigh Ritz Autoencoder (RRAE). It is suitable for
applications in instrumentation and measurement where the system being
observed by multiple sensors is well modelled as a boundary value problem.
The embedding of the admissible functions in the decoder implements a truly
physics-informed machine learning architecture. The RRAE provides an exact
fulfilment of Neumann, Cauchy, Dirichlet or periodic constraints. Only the
encoder needs to be trained; consequently, the RRAE is numerically more
efficient during training than traditional autoencoders.

The new Rayleigh-Ritz Autoencoder has been applied to an instrumentation
and measurement problem in structural monitoring. It involves the fusion
of data from multiple sensors and the solution of a boundary value problem.
A 1-norm minimization has been chosen to minimize the effects of
non-Gaussian perturbations and to demonstrate the non-linear abilities of
the RRAE. The results from the tunnel monitoring application over months of
work are presented in detail."
Titel2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (I2MTC 2023)
ErscheinungsortKuala Lumpur, Malaysia
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
PublikationsstatusVeröffentlicht - 2023
Veranstaltung2023 IEEE International Instrumentation and Measurement Technology
Conference (I2MTC) (I2MTC 2023): nstrumentation and Measurement: Rising Above Covid-19
- Kuala Lumpur, Malaysia
Dauer: 22 Mai 202325 Mai 2023


Konferenz2023 IEEE International Instrumentation and Measurement Technology
Conference (I2MTC) (I2MTC 2023)
KurztitelI2MTC 2023
OrtKuala Lumpur

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