Letture consigliate

Sadowski T., Trovalusci P. Eds. (2014): Multiscale Modelling of Complex Materials, CISM Courses and Lectures No. 403, International Centre for Mechanical Sciences, Wien NewYork. Springer, Vol. 556. Ghosh S. (May 2024), Machine learning-enabled parametrically upscaled constitutive models for bridging length scales in Ti and Ni alloys, in: Innovative Lightweight and High-Strength Alloys, Mohammed A. Zikry (Ed.), Elsevier. Weber G., Pinz M. and Ghosh S. (December 2022), Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys, Computer Methods in Applied Mechanics and Engineering, Vol. 402, Art. 115384. Trovalusci P., Pau A. (2014), Derivation of microstructured continua from lattice systems via principle of virtual works. The case of masonry-like materials as micropolar, second gradient and classical continua. Acta Mechanica, 225 (1), 157-177. Trovalusci P., Ed. (2016): Materials with Internal Structure. Multiscale and Multifield Modeling and Simulation, Springer Tracts in Mech Engn Series, Springer, Vol 18. Xu, Y., Kuang, Z., Yang, J., Huang, Q., Huang, W., Hu, H. (2024), Quantum computing enhanced distance-minimizing data-driven computational mechanics, Computer Methods in Applied Mechanics and Engineering 419, 116675. Yang, J., Huang, W., Huang, Q., Hu, H. (2022), An investigation on the coupling of data-driven computing and model-driven computing. Computer Methods in Applied Mechanics and Engineering 393, 114798. Postek E., Sadowski T. (2021), Impact model of the Al 2 O 3 /ZrO 2 composite by peridynamics, Compos. Struct. 271, 114071. Schmauder, S., Schäfer, I. Eds. (2016), Multiscale Materials Modeling: Approaches to Full Multiscaling, De Gruyter.