We introduce an efficient computational framework for the simulation of complex beam networks and architected materials. At its core stands a thermodynamics-informed neural network, which serves as a surrogate material model for the cross-sectional respons ...
More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...
Architected materials achieve unique mechanical properties through precisely engineered microstructures that minimize material usage. However, a key challenge of low-density materials is balancing high stiffness with stable deformability up to large strain ...
This study elucidates the fundamental governing mechanisms behind necking instability in granular materials, a phenomenon extensively documented in the literature yet lacking a clear explanation of its underlying causes. Our findings suggest that the pheno ...
We develop a model for predicting the mechanical behavior of a system of slender one-dimensional bodies (fibers or beams) interacting via frictional contact. Relying on an integral penalty-based formulation, it can robustly capture the behavior in the case ...
This study investigates the underlying mechanisms causing necking instability in granular materials. Using a digital twin created with the level-set discrete element method (LS-DEM), we simulate triaxial compression and extension cycles on a Hostun sand sp ...