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Archives of Computational Methods in Engineering

, Volume 25, Issue 1, pp 143–164 | Cite as

Big Data in Experimental Mechanics and Model Order Reduction: Today’s Challenges and Tomorrow’s Opportunities

  • Jan Neggers
  • Olivier Allix
  • François Hild
  • Stéphane Roux
S.I.: Machine learning in computational mechanics

Abstract

Since the turn of the century experimental solid mechanics has undergone major changes with the generalized use of images. The number of acquired data has literally exploded and one of today’s challenges is related to the saturation of mining procedures through such big data sets. With respect to digital image/volume correlation one of tomorrow’s pathways is to better control and master this data flow with procedures that are optimized for extracting the sought information with minimum uncertainties and maximum robustness. In this paper emphasis is put on various hierarchical identification procedures. Based on such structures a posteriori model/data reductions are performed in order to ease and make the exploitation of the experimental information far more efficient. Some possibilities related to other model order reduction techniques like the proper generalized decomposition are discussed and new opportunities are sketched.

Notes

Acknowledgements

It is a pleasure to acknowledge the support of BPI France within the “DICCIT” project.

Compliance with Ethical Standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© CIMNE, Barcelona, Spain 2017

Authors and Affiliations

  1. 1.LMT, ENS Paris-Saclay, CNRS, Université Paris-SaclayCachanFrance
  2. 2.Institut Universitaire de FranceParisFrance

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