Tsialiamanis, G. orcid.org/0000-0002-1205-4175, Dervilis, N. orcid.org/0000-0002-5712-7323, Wagg, D.J. orcid.org/0000-0002-7266-2105 et al. (1 more author) (2023) Towards a population-informed approach to the definition of data-driven models for structural dynamics. Mechanical Systems and Signal Processing, 200. 110581. ISSN 0888-3270
Abstract
Machine learning has affected the way in which many phenomena for various domains are modelled, one of these domains being that of structural dynamics. However, because machine-learning algorithms are problem-specific, they often fail to perform efficiently in cases of data scarcity. To deal with such issues, combination of physics-based approaches and machine learning algorithms have been developed. Although such methods are effective, they also require the analyser's understanding of the underlying physics of the problem. The current work is aimed at motivating the use of models which learn such relationships from a population of phenomena, whose underlying physics are similar. The development of such models is motivated by the way that physics-based models, and more specifically finite element models, work. Such models are considered transferable, explainable and trustworthy, attributes which are not trivially imposed or achieved for machine-learning models. For this reason, machine-learning approaches are less trusted by industry and often considered more difficult to form validated models. To achieve such data-driven models, a population-based scheme is followed here and two different machine-learning algorithms from the meta-learning domain are used. The two algorithms are the model-agnostic meta-learning (MAML) algorithm and the conditional neural processes (CNP) model. The two approaches have been developed to perform within a population of tasks and, herein, they are tested on a simulated dataset of a population of structures, with data available from a small subset of the population. Such situations are considered to be similar to having data available from existing structures or structures in a laboratory environment or even from a model and needing to model a new structure with only a few available data samples. The algorithms seem to perform as intended and outperform a traditional machine-learning algorithm at approximating the quantities of interest. Moreover, they exhibit behaviour similar to traditional machine learning algorithms (e.g. neural networks or Gaussian processes), concerning their performance as a function of the available structures in the training population, i.e. the more training structures, the better and more robustly the algorithms learn the underlying relationships.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in Mechanical Systems and Signal Processing is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Structural dynamics; Machine learning; Population-based modelling; Transfer learning; Meta-learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/R006768/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Aug 2023 10:59 |
Last Modified: | 04 Sep 2023 11:21 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ymssp.2023.110581 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202126 |