{"id":29675144,"url":"https://github.com/trixi-framework/paper-2025-particle-based_preprocessing","last_synced_at":"2026-02-06T05:32:54.245Z","repository":{"id":299812054,"uuid":"1003516545","full_name":"trixi-framework/paper-2025-particle-based_preprocessing","owner":"trixi-framework","description":"Reproducibility repository for the paper \"Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary 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returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-07-22T23:06:07.661Z","updated_at":"2026-02-06T05:32:54.229Z","avatar_url":"https://github.com/trixi-framework.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-success.svg)](https://opensource.org/licenses/MIT)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15730554.svg)](https://doi.org/10.5281/zenodo.15730554)\n[![doi:10.1016/j.cpc.2025.109898](https://img.shields.io/badge/Paper_DOI-10.1016/j.cpc.2025.109898-blue)](https://doi.org/10.1016/j.cpc.2025.109898)\n[![arXiv:2506.21206](https://img.shields.io/badge/arXiv-2506.21206-yellow)](https://arxiv.org/abs/2506.21206)\n\nThis repository contains information and code to reproduce the results presented in the\narticle\n```bibtex\n@Article{neher2026robust,\n  author    = {Neher, Niklas S. and Faulhaber, Erik and Berger, Sven and Weißenfels,\n               Christian and Gassner, Gregor J. and Schlottke-Lakemper, Michael},\n  journal   = {Computer Physics Communications},\n  title     = {Robust and efficient pre-processing techniques for particle-based\n               methods including dynamic boundary generation},\n  year      = {2026},\n  pages     = {109898},\n  volume    = {318},\n  doi       = {10.1016/j.cpc.2025.109898}\n}\n```\n\nIf you find these results useful, please cite the article mentioned above. If you\nuse the implementations provided here, please **also** cite this repository as\n```bibtex\n@misc{Neher2025reproducibility,\n  title={Reproducibility repository for\n         \"{R}obust and efficient pre-processing techniques for particle-based\n         methods including dynamic boundary generation\"},\n  author={Neher, Niklas S. and Faulhaber, Erik and Berger, Sven\n          and Weißenfels Christian and Gassner, Gregor J. and Schlottke-Lakemper, Michael},\n  year= {2025},\n  howpublished={\\url{https://github.com/trixi-framework/paper-2025-particle-based_preprocessing}},\n  doi={10.5281/zenodo.15730554}\n}\n```\n\n\n## Abstract\n\nObtaining high-quality particle distributions for stable and accurate particle-based simulations poses significant challenges, especially for complex geometries.\nWe introduce a preprocessing technique for 2D and 3D geometries, optimized for smoothed particle hydrodynamics (SPH) and other particle-based methods.\nOur pipeline begins with the generation of a resolution-adaptive point cloud near the geometry's surface employing a face-based neighborhood search.\nThis point cloud forms the basis for a signed distance field,\nenabling efficient, localized computations near surface regions.\nTo create an initial particle configuration, we apply a hierarchical winding number method for fast and accurate inside-outside segmentation.\nParticle positions are then relaxed using an SPH-inspired scheme, which also serves to pack boundary particles.\nThis ensures full kernel support and promotes isotropic distributions while preserving the geometry interface.\nBy leveraging the meshless nature of particle-based methods,\nour approach does not require connectivity information and is thus straightforward to integrate into existing particle-based frameworks.\nIt is robust to imperfect input geometries and memory-efficient without compromising performance.\nMoreover, our experiments demonstrate that with increasingly higher resolution, the\nresulting particle distribution converges to the exact geometry.\n\n\n## Numerical experiments\n\nThe numerical experiments presented in the paper use\n[TrixiParticles.jl](https://github.com/trixi-framework/TrixiParticles.jl).\nTo reproduce the numerical experiments, you need to install\n[Julia](https://julialang.org/).\n\nThe subfolder `code` of this repository contains a `README.md` file with\ninstructions to reproduce the numerical experiments.\nThe subfolders also include the input data, result data and scripts for postprocessing.\n\nAll numerical experiments were carried out using Julia v1.11.5.\n\n## Authors\n\n- Niklas S. Neher\n- Erik Faulhaber\n- Sven Berger\n- Christian Weißenfels\n- Gregor J. Gassner\n- Michael Schlottke-Lakemper\n\n## License\n\nThe contents of this repository are available under the [MIT license](LICENSE.md). If you reuse our\ncode or data, please also cite us (see above).\n\n\n## Disclaimer\n\nEverything is provided as is and without warranty. Use at your own risk!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrixi-framework%2Fpaper-2025-particle-based_preprocessing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftrixi-framework%2Fpaper-2025-particle-based_preprocessing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrixi-framework%2Fpaper-2025-particle-based_preprocessing/lists"}