Calibration of yield criterion parameters for composite materials within a micropolar continuum framework ABSTRACT The mechanical behaviour of composite materials is strongly influenced by microscale heterogeneities such as voids, cracks, and particles, which make their macromechanical description non-trivial. Non-local frameworks, including micropolar theory, provide an effective approach to account for microstructural size effects [1]. A dual modelling approach is adopted, in which the materials are analysed both as discrete systems of rigid particles interacting via elasto–perfectly plastic springs and as equivalent elasto–perfectly plastic continua. A multiscale identification procedure is employed to derive the elastic constitutive parameters [1], while the parameters governing the yield domain [2] are calibrated through an optimisation strategy based on the Differential Evolution (DE) algorithm [3, 4]. Numerical simulations show the effectiveness of the proposed methodology in bridging discrete and continuum descriptions, enabling the identification of complex constitutive parameters that are often unknown or determined through parametric sensitivity analyses. The proposed framework supports practical applications of equivalent micropolar models for materials with complex microstructures and provides a basis for possible experimental validation. REFERENCES [1] Trovalusci, P., & Masiani, R. (1999). Material symmetries of micropolar continua equivalent to lattices. International Journal of Solids and Structures, 36(14), 2091-2108. [2] Forest, S., & Sievert, R. (2003). Elastoviscoplastic constitutive frameworks for generalized continua. Acta Mechanica, 160(1), 71-111 [3] Colatosti, M., Carboni, B., Fantuzzi, N., & Trovalusci, P. (2023). Composite material identification as micropolar continua via an optimization approach. Composites Part C: Open Access, 11, 100362. [4] Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11, 341-359. Acknowledgments: This work is supported by MIUR, project: National Center on HPC, Big Data and Quantum Computing (ICSC), PNRR - CN1- Spoke6 (CUP: B83C22002940006), and PROGETTO MUSICS -