A simple and performant Python implementation of the bond-based peridynamic model. PyPD offers an intuitive class structure with fully interchangeable material models and integration schemes.

• Pure Python: Written entirely in Python, leveraging the power of Numba for optimal performance
• Material Models: Seamlessly switch between various material models including linear, trilinear, and nonlinear
• Integration schemes: Fully interchangeable integration schemes
• Examples: Several examples are provided and validated using published experimental data

Explore examples using PyPD in Google Colab

Example description	Notebook
Crack branching in Homalite
Half-notched quasi-brittle beam in three-point bending
Plate with a hole under tension

Development version from GitHub:

$ pip install git+https://github.com/mark-hobbs/PyPD.git

or for contributors using Pipenv:

$ git clone https://github.com/mark-hobbs/PyPD.git
$ cd PyPD/
$ pipenv install --dev
$ pipenv shell

• NumPy
• Numba
• scikit-learn
• Matplotlib
• tqdm

Development dependencies

• Black
• Ruff
• Jupyter

python -m examples.2D_notch.py

python -m examples.2D_B4_HN.py

• Write unit tests
• Write documentation
• Publish on PyPI
• feature/space-filling-curve - sort particles spatially to improve memory access (see this notebook on understanding the Hilbert curve)
• feature/animation - add native capabilities to generate animations
• GPU acceleration (see this notebook where pytorch is used to speed up particle simulations)
• Implement a volume correction scheme to improve spatial integration accuracy
• Implement a surface correction scheme to correct the peridynamic surface effect
• Implement different influence functions (constant/triangular/quartic)