During the EGU2023 conference, when I presented a high-performance MPM (Material Point Method) solver, I was asked, "How do you discretize the computational model for the MPM?" I didn't have a clear answer (I didn't even consider it a problem) because the models were relatively simple and could be generated directly using some straightforward functions. However, as computational models gradually became more complex and diverse, I began to realize that this was indeed a very good question. The preprocessing for MPM should not be a computationally intensive task; it should be fast enough. Yet, I couldn't find a "plug-and-play" generalized code for this purpose. Some literatures have contributed to this issue, and I built upon their work to create a comprehensive and refined julia package.

No parallelization, no problem—5,334,808 particles from an STL file (998,137 triangles) in just 0.6 s.

Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz

Just type ] in Julia's REPL:

julia> ]
(@1.11) Pkg> add MaterialPointGenerator

• Structured (regular) coordinates
• Support complicated 2/3D models
• Particle generation from a Digital Elevation Model (DEM) file
• Automatically interpolate DEM files with support for shape trimming
• Attach attributions to the particles
• SLBL and boundary selector interface

3D phoenix and dragon	DEM with thickness	complex 2D

2D landslide profile with geological structure (nid)

3D DEM with material ID	Profile

SLBL

If you find MaterialPointGenerator.jl useful or have used it in your research, please cite it as follows:

@article{Huo2025,
  author  = {Huo, Zenan
             and Zheng, Xiangcou
             and Jaboyedoff, Michel
             and Podladchikov, Yury
             and Mei, Gang
             and Tang, Xiong},
  title   = {An efficient framework for structured material particle generation in multi-context modeling},
  journal = {Engineering with Computers},
  year    = {2025},
  month   = {Oct},
  day     = {17},
  issn    = {1435-5663},
  doi     = {10.1007/s00366-025-02222-z},
  url     = {https://doi.org/10.1007/s00366-025-02222-z}
}

This project is sponserd by Risk Group | Université de Lausanne and China Scholarship Council [中国国家留学基金管理委员会].