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Influence of a Topology of a Spring Network on its Ability to Learn Mechanical Behaviour

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Parallel Processing and Applied Mathematics (PPAM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8384))

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Abstract

We discuss how the topology of the spring system/network affects its ability to learn a desired mechanical behaviour. To ensure such a behaviour, physical parameters of springs of the system are adjusted by an appropriate gradient descent learning algorithm. We find the betweenness centrality measure particularly convenient to describe topology of the spring system structure with the best mechanical properties. We apply our results to refine an algorithm generating the structure of a spring network. We also present numerical results confirming our statements.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland

    Maja Czoków

  2. Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland

    Jacek Miękisz

Authors
  1. Maja Czoków
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  2. Jacek Miękisz
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Corresponding author

Correspondence to Maja Czoków .

Editor information

Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Department of Computer Science, Knoxville, Tennessee, USA

    Jack Dongarra

  3. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

  4. Technical University of Denmark Informatics and Mathematical Modelling, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Czoków, M., Miękisz, J. (2014). Influence of a Topology of a Spring Network on its Ability to Learn Mechanical Behaviour. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_39

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  • DOI: https://doi.org/10.1007/978-3-642-55224-3_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55223-6

  • Online ISBN: 978-3-642-55224-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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