The repository contains two folders: one for the Lattice Spring Model (LSM) code and the other for the Machine Learning (ML) code used in the corresponding mechanics examples reported in the paper "A Bio-lattice Deep Learning Framework for Modeling Discrete Biological Materials".

To provide an example of how the system performs, we have included specific values for the spring stiffness in both the homogeneous and composite cases. These values demonstrate the behavior of the lattice system under different microstructural configurations. Users can modify these values to explore the effects of varying stiffness parameters on the stress-strain response and microstructural mechanics. This serves as a starting point for understanding how the framework handles different scenarios.

The dataset produced by this code is subsequently used for training the neural network.

The ML models can be readily used; after training the model, the weights need to be saved. These trained weights are integrated into the MOOSE framework to perform finite element method (FEM) simulations.

The code is provided as developed by the authors. Users should adapt the LSM code to their own datasets and specific needs. The ML code can be used as-is but should be trained on the appropriate dataset.

If you use or edit our work, please cite the paper