Abstract
The use of constitutive equations calibrated from data has been implemented into standard numerical solvers for successfully addressing a variety problems encountered in simulation-based engineering sciences (SBES). However, the complexity remains constantly increasing due to the need of increasingly detailed models as well as the use of engineered materials. Data-Driven simulation constitutes a potential change of paradigm in SBES. Standard simulation in computational mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,\(\ldots \)), whereas the second one consists of models that scientists have extracted from collected, either natural or synthetic, data. Data-driven (or data-intensive) simulation consists of directly linking experimental data to computers in order to perform numerical simulations. These simulations will employ laws, universally recognized as epistemic, while minimizing the need of explicit, often phenomenological, models. The main drawback of such an approach is the large amount of required data, some of them inaccessible from the nowadays testing facilities. Such difficulty can be circumvented in many cases, and in any case alleviated, by considering complex tests, collecting as many data as possible and then using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.
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Acknowledgements
This work has been supported by the ESI GROUP Chair at Ecole Centrale of Nantes as well as by the Spanish Ministry of Economy and Competitiveness, through grants number CICYT DPI2014-51844-C2-1-R and DPI2015-72365-EXP and by the Regional Government of Aragon and the European Social Fund, research group T88. This support is gratefully acknowledged.
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Ibañez, R., Borzacchiello, D., Aguado, J.V. et al. Data-driven non-linear elasticity: constitutive manifold construction and problem discretization. Comput Mech 60, 813–826 (2017). https://doi.org/10.1007/s00466-017-1440-1
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DOI: https://doi.org/10.1007/s00466-017-1440-1