Material data identification in generalized continua
Jacinto Ulloa, Laurent Stainier
TL;DR
The paper introduces a model-free data-driven framework to identify generalized micromorphic material data directly from full-field kinematics and boundary forces by enforcing non-classical balance laws. It formulates the problem in a micromorphic phase space, uses clustering to build representative stress–strain datasets, and solves an equilibrium-focused data-identification task that yields non-symmetric and higher-order stresses. Validation on synthetic micromorphic data and a mechanical metamaterial (honeycomb) demonstrates accurate recovery of generalized stresses and successful data-driven predictions for unseen configurations. This approach provides a practical route to characterize microstructured solids without prescriptive constitutive models, enabling calibration and model-free simulations in generalized continua.
Abstract
We introduce a data-driven framework for identifying material behavior from full-field kinematics and force measurements in generalized (micromorphic) continua. Unlike traditional approaches that rely on constitutive assumptions or homogenization schemes, our method extracts generalized stress--strain data by enforcing non-classical balance laws and compatibility relations on full-field boundary value problems. Specifically, the approach infers the associated generalized stresses and constructs representative material datasets via clustering in a non-classical phase space. We show that the proposed method reliably extracts non-symmetric and higher-order local stress states, providing material data suitable for either model calibration or model-free data-driven simulations of generalized continua. These capabilities are demonstrated in validation simulations with synthetic data and in an application to mechanical metamaterials, suggesting a practical route for material characterization of microstructured solids.
