Consideration of Polymorphic Uncertainty in Model-Free Data-Driven Identification of Stress-Strain Relations

The method of data-driven identification, introduced by Leygue et al. (2018), enables the determination of large stress-strain data sets based on displacement fields and applied boundary conditions without postulating a specific constitutive model. The algorithm has shown to be applicable to synthetic and real data taking linear as well as non-linear material behavior into account (Dalémat et al. (2019)). The consideration of uncertain material properties by data-driven approaches, e.g. shown in Zschocke et al. (2022), leads to the requirement of data sets representing uncertain material behavior. In this contribution, different sources of uncertainty occurring within the identification of stress-strain relations are addressed and an efficient method for the identification of data sets representing uncertain material behavior based on the concept of data-driven identification is proposed. In order to demonstrate the introduced methods, numerical examples are carried out.

Keywords: Data-driven identification, Polymorphic uncertainty, Data-driven computational mechanics, Data science.

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