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Model-free data-driven computational mechanics enhanced by tensor voting

Eggersmann, Robert and Stainier, Laurent and Ortiz, Michael and Reese, Stefanie (2021) Model-free data-driven computational mechanics enhanced by tensor voting. Computer Methods in Applied Mechanics and Engineering, 373 . Art. No. 113499. ISSN 0045-7825. https://resolver.caltech.edu/CaltechAUTHORS:20201015-152733627

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Abstract

The data-driven computing paradigm initially introduced by Kirchdoerfer and Ortiz (2016) is extended by incorporating locally linear tangent spaces into the data set. These tangent spaces are constructed by means of the tensor voting method introduced by Mordohai and Medioni (2010) which improves the learning of the underlying structure of a data set. Tensor voting is an instance-based machine learning technique which accumulates votes from the nearest neighbors to build up second-order tensors encoding tangents and normals to the underlying data structure. The here proposed second-order data-driven paradigm is a plug-in method for distance-minimizing as well as entropy-maximizing data-driven schemes. Like its predecessor (Kirchdoerfer and Ortiz, 2016), the resulting method aims to minimize a suitably defined free energy over phase space subject to compatibility and equilibrium constraints. The method’s implementation is straightforward and numerically efficient since the data structure analysis is performed in an offline step. Selected numerical examples are presented that establish the higher-order convergence properties of the data-driven solvers enhanced by tensor voting for ideal and noisy data sets.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.cma.2020.113499DOIArticle
ORCID:
AuthorORCID
Stainier, Laurent0000-0001-6719-6616
Ortiz, Michael0000-0001-5877-4824
Additional Information:© 2020 Elsevier. Received 6 April 2020, Revised 7 October 2020, Accepted 8 October 2020, Available online 15 October 2020. MO gratefully acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG), Germany through the Sonderforschungsbereich 1060 “The mathematics of emergent effects”. SR and RE gratefully acknowledge the financial support of the Deutsche Forschungsgemeinschaft (DFG) through the project RE 1057/40-2 “Model order reduction in space and parameter dimension — towards damage-based modeling of polymorphic uncertainty in the context of robustness and reliability” within the priority program SPP 1886 “Polymorphic uncertainty modelling for the numerical design of structures”. Further, SR and RE rewardingly acknowledge the funding by the Excellence Initiative of the German federal and state governments through the project “Predictive Hierarchical Simulation”. Finally, all authors acknowledge the financial support of the DFG and French Agence Nationale de la Recherche (ANR) through the project “Direct Data-Driven Computational Mechanics for Anelastic Material Behaviours” (project numbers: ANR-19-CE46-0012-01, RE 1057/47-1) within the French–German Collaboration for Joint Projects in Natural, Life and Engineering (NLE) Sciences. Declaration of Competing Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Deutsche Forschungsgemeinschaft (DFG)Sonderforschungsbereich 1060
Deutsche Forschungsgemeinschaft (DFG)RE 1057/40-2
Deutsche Forschungsgemeinschaft (DFG)SPP 1886
Agence Nationale pour la Recherche (ANR)ANR-19-CE46-0012-01
Deutsche Forschungsgemeinschaft (DFG)RE 1057/47-1
Subject Keywords:Data-driven computing; Tensor voting; Second-order method; Data science
Record Number:CaltechAUTHORS:20201015-152733627
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201015-152733627
Official Citation:Robert Eggersmann, Laurent Stainier, Michael Ortiz, Stefanie Reese, Model-free data-driven computational mechanics enhanced by tensor voting, Computer Methods in Applied Mechanics and Engineering, Volume 373, 2021, 113499, ISSN 0045-7825, https://doi.org/10.1016/j.cma.2020.113499.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106095
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:16 Oct 2020 14:48
Last Modified:23 Nov 2020 20:35

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