Letture consigliate

P. Wriggers, F. Aldakheel, B. Hudobivnik (2023), Virtual Element Methods in Engineering Sciences, Springer. P. F. Antonietti, L. Beirao da Veiga, G. Manzini (2022), The Virtual Element Method and its Applications, SEMA SIMAI Springer series Vol 31, Springer. J. Schröder, A. Schwarz, K. Steeger (2016), Least-Squares Mixed Finite Element Formulations for Isotropic and Anisotropic Elasticity at Small and Large Strains, in: Schröder, J., Wriggers, P. (eds) Advanced Finite Element Technologies. CISM Vol 566. Springer. A. Schwarz, K. Steeger, M. Igelbüscher, J. Schröder (2018), Different approaches for mixed LSFEMs in hyperelasticity: Application of logarithmic deformation measure, IJNME, 115, 1138–115. S. Kumar, D. M. Kochmann (2022), What machine learning can do for computational solid mechanics. In Current trends and open problems in computational mechanics (pp. 275-285), Cham: Springer. J. N. Fuhg, G. A. Padmanabha, N. Bouklas, ..., L. De Lorenzis (2024), A review on data-driven constitutive laws for solid, arXiv:2405.0365. O. Boolakee, M. Geier, L. De Lorenzis (2023), A new Lattice Boltzmann scheme for linear elastic solids: periodic problems, CMAME, 404: 115756. O. Boolakee, M. Geier, L. De Lorenzis. Lattice Boltzmann for linear elastodynamics: periodic problems and Dirichlet boundary conditions, https://arxiv.org/abs/2408.01081. M. Horak, A.J. Gil, R. Ortigosa, M. Kruzik (2023), A polyconvex transversely-isotropic invariant based formulation for electro-mechanics: stability, minimisers and computational implementation, CMAME, 403: 115695. F. Marín, J. Martínez-Frutos, R. Ortigosa, A.J. Gil (2021), Convex Multi-Variable based Computational Framework for Multilayered Electro-Active Polymers, CMAME, 374: 113567. J. Korelc, P. Wriggers (2016), Automation of finite element methods, Springer.