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Published May 1, 2023 | metadata_only
Journal Article

Adaptive goal-oriented data sampling in Data-Driven Computational Mechanics


Data-Driven (DD) computing is an emerging field of Computational Mechanics, motivated by recent technological advances in experimental measurements, the development of highly predictive computational models, advances in data storage and data processing, which enable the transition from a material data-scarce to a material data-rich era. The predictive capability of DD simulations is contingent on the quality of the material data set, i.e. its ability to closely sample all the strain–stress states in the phase space of a given mechanical problem. In this study, we develop a methodology for increasing the quality of an existing material data set through iterative expansions. Leveraging the formulation of the problems treated with the DD paradigm as distance minimization problems, we identify regions in phase space with poor data coverage, and target them with additional experiments or lower-scale simulations. The DD solution informs the additional experiments so that they can provide better coverage of the phase space of a given application.

Additional Information

© 2023 Elsevier. L. Stainier and M. Ortiz gratefully acknowledge the financial support of the Deutsche Forschungsgemeinschaft (DFG) and French Agence Nationale de la Recherche (ANR) through the project "Direct Data-Driven Computational Mechanics for Anelastic Material Behaviours" (ANR-19-CE46-0012-01, RE 1057/47-1, project number 431386925) within the French-German Collaboration for Joint Projects in Natural, Life and Engineering (NLE) Sciences. L. Stainier acknowledges the support from the "NExT program of Nantes Université, through the iDDrEAM IRP project". M. Ortiz is grateful for support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via project 211504053 - SFB 1060; project 441211072 - SPP 2256; and project 390685813 - GZ 2047/1 - HCM. A. Gorgogianni acknowledges the financial support provided by the Drinkward fellowship of the California Institute of Technology. Data availability. Data will be made available on request. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.E. Andrade reports financial support was provided by National Science Foundation. J.E. Andrade reports financial support was provided by US Army Research Office.

Additional details

August 22, 2023
August 22, 2023