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Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach

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Abstract

Data-driven computing in applied mechanics utilizes the material data set directly, and hence is free from errors and uncertainties stemming from the conventional material modeling. For data-driven computing in elasticity, this paper presents a simple heuristic that is robust against noise and outliers in a data set. For each structural element, we extract the material property from some nearest data points. Using the nearest neighbors reduces the influence of noise, compared with the existing method that uses a single data point. Also, the robust regression is adopted to reduce the influence of outliers. Numerical experiments on the static equilibrium analysis of trusses are performed to illustrate that the proposed method is robust against the presence of noise and outliers.

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Notes

  1. Since this example is a statically determinate truss, for any member strains there exists a compatible nodal displacement vector. What is meant to be explained here is that in the proposed method a solution is alway defined so as to satisfy the compatibility relation exactly.

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Acknowledgements

This work is partially supported by JSPS KAKENHI 17K06633 and 18K18898.

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Authors and Affiliations

  1. Mathematics and Informatics Center, The University of Tokyo, Hongo 7-3-1, Tokyo, 113-8656, Japan

    Yoshihiro Kanno

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  1. Yoshihiro Kanno
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Correspondence to Yoshihiro Kanno.

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Kanno, Y. Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach. Japan J. Indust. Appl. Math. 35, 1085–1101 (2018). https://doi.org/10.1007/s13160-018-0323-y

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  • DOI: https://doi.org/10.1007/s13160-018-0323-y

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