European Journal of Mechanics / A Solids

Data accompanying the research paper: Čanađija, M., Košmerl, V., Zlatić, M., Vrtovšnik, D., Munjas, N.: A computational framework for nanotrusses: input convex neural networks approach, European Journal of Mechanics - A/Solids (2023) Trained neural networks for true-stress vs. true strain uniaxial tension/compression curves and diameter vs. true strain for 818 different single-walled carbon nanotubes at 300 K. Datasets obtained by molecular dynamics (MD) are also enclosed. Results used in the second example in the above paper, obtained by MD are provided. In the case you find this dataset useful, please cite the above paper. Full bibliographic data can be found at the DOI link: https://doi.org/10.1016/j.euromechsol.2023.105195

These are the source files for the finite-strain Raviart-Thomas element, making use of Mathematica and Acegen.

Abaqus Python scripts for the analysis of laminar and fibrous infinite media undergoing a jamming transition

A Method for Selection of Structural Theories for Low to High Frequency Vibration Analyses HIGHER-ORDER WAVE PARAMETERS OF FLEXURAL BEAM VIBRATION FROM LOW TO HIGH FREQUENCIES