Speaker
Description
The modeling of non-metals presents significant challenges due to the complex mechanical behavior of such materials. For instance, the constitutive parameters of polymers are often calibrated using homogeneous experimental data, which are typically oversimplified. Moreover, even when these models successfully replicate homogeneous experimental results, there have been no mathematical guarantees for reliable predictions in real-world applications. In this contribution, we present a novel data-driven computational method that converts large datasets on the damage-elastoplasticity of elastomers into constitutive equations that accurately predict behavior even for new, unseen cases. To this end, a regression algorithm is developed that learns polymer mechanics from full field measurement data. By means of an elegant model selection technique, modest generalization error of the proposed data-driven statistical learning framework can be guaranteed. The presented approach establishes a foundation for generalization using mechanical data, a capability not present in traditional constitutive modeling techniques.
