HookeAI: An open-source ADiMU framework
Classical LAMinate theory for the lightweight design of structures and systems in PYthon
Interactive Streamlit tool for predicting directional yield strength in steel using the Hill 1948 anisotropy criterion. Adjust Lankford coefficients (r0, r45, r90) in real time and visualise the effect on the yield curve.
Python librarY for material characteriZation based on experImental dAta for lightweiGht desigN
Collection of fiber-reinforced concrete specimen files (IGES, CAD) imported into Abaqus, designed as examples for learning.
Convex signed singular value neural network (CSSV-NN) for isotropic hyperelasticity: learns a strain-energy density from stress data alone, with objectivity, isotropy, polyconvexity and thermodynamic consistency guaranteed by construction rather than by penalty terms. Reference implementation for Geuken et al., JMPS 203 (2025).
HyperSmart is a Python-based scientific tool for automated hyperelastic model selection, parameter calibration, and centralized experimental data management. It features a Tkinter GUI, YAML data storage, and numerical methods (Enumeration & Bayesian Updating) for material modeling.
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