SynxFlow
SynxFlow: A GPU-accelerated Python package for multi-hazard simulations - Published in JOSS (2025)
Science Score: 95.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
✓Committers with academic emails
1 of 6 committers (16.7%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Simulates flood, landslide and debris flow dynamically using GPUs
Basic Info
- Host: GitHub
- Owner: SynxFlow
- License: gpl-3.0
- Language: C++
- Default Branch: master
- Homepage: https://synxflow.readthedocs.io
- Size: 5.78 MB
Statistics
- Stars: 60
- Watchers: 5
- Forks: 8
- Open Issues: 6
- Releases: 3
Topics
Metadata Files
README.md
SynxFlow: Synergising High-Performance Hazard Simulation with Data Flow
What the software can do
This software can dynamically simulate flood inundation, landslides runout and debris flows using multiple CUDA-enabled GPUs. It also offers an user-friendly yet versatile Python interface that can be fully integrated into data science workflows, aiming to streamline and accelerate hazard risk assessment tasks.
Using the model
For how to install and use the model, please visit here
Running the model on Google Colab
The tutorials can also run on Google Colab by using one of the following links
Click me to run a flood simulation
Click me to run a landslide runout simulation
Click me to run a debris flow simulation
Using LLMs to control SynxFlow
It is also possible to use Large Language Models (LLMs) to generate script to use SynxFlow and build complex workflows.
Please see this tutorial to learn how to do this.
Acknowledgment
SynxFlow represents our distinct vision for the next generation of tools in this field, aiming to address evolving challenges and user needs with cutting-edge technologies. Our goal is to offer powerful, yet user-friendly tools for research and industrial applications, ensuring broad accessibility and applicability. In this spirit, SynxFlow is committed to being an open-source, community-driven and inclusive project. SynxFlow inherits code from established open-source software such as HiPIMS-CUDA [1] and Pypims [2]. The development of SynxFlow has also benefited from the skills, knowledge, and experience gained by its authors while contributing as main developers to HiPIMS-CUDA and Pypims.
[1] HiPIMS stands for High-Performance Integrated hydrodynamic Modelling System. HiPIMS is an open source flood Modelling suite developed and maintained by Prof Qiuhua Liang and his team in Loughborough University.
[2] Pypims is a further development of HiPIMS-CUDA to provide a Python interface.
Owner
- Login: SynxFlow
- Kind: user
- Repositories: 1
- Profile: https://github.com/SynxFlow
JOSS Publication
SynxFlow: A GPU-accelerated Python package for multi-hazard simulations
Authors
School of Engineering, University of Birmingham, Birmingham, UK
Ping An P&C Insurance Company of China, Shenzhen, China
Editor
Anjali Sandip
Tags
flood landslide debris flow GPUGitHub Events
Total
- Create event: 3
- Release event: 2
- Issues event: 17
- Watch event: 27
- Issue comment event: 11
- Push event: 20
- Pull request event: 7
- Fork event: 3
Last Year
- Create event: 3
- Release event: 2
- Issues event: 17
- Watch event: 27
- Issue comment event: 11
- Push event: 20
- Pull request event: 7
- Fork event: 3
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Xilin Xia | x****9@g****m | 110 |
| Xiaodong Ming | x****g@o****m | 32 |
| Xilin Xia | x****2@l****k | 9 |
| thivinanandh | t****h@g****m | 4 |
| SynxFlow | 1****w | 2 |
| pypims | 6****s | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 19
- Total pull requests: 7
- Average time to close issues: 4 days
- Average time to close pull requests: about 1 month
- Total issue authors: 9
- Total pull request authors: 4
- Average comments per issue: 1.47
- Average comments per pull request: 0.43
- Merged pull requests: 6
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 15
- Pull requests: 6
- Average time to close issues: 5 days
- Average time to close pull requests: about 1 month
- Issue authors: 7
- Pull request authors: 3
- Average comments per issue: 0.8
- Average comments per pull request: 0.0
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- zhangrf21 (6)
- weiyilan (3)
- jennykupzig (3)
- olbermann (2)
- barneydobson (1)
- yybnb (1)
- thivinanandh (1)
- Kong-JiaTao (1)
- kinghuihui9999 (1)
Pull Request Authors
- thivinanandh (4)
- mingxiaodong (2)
- xiaxilin (2)
- longyangzz (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- fiona *
- imageio *
- matplotlib *
- myst-parser *
- nbsphinx *
- numpy *
- pandas *
- pyshp *
- rasterio *
- scipy *
- sphinx >=4.1
- sphinx-rtd-theme >=1.0.0
- fiona *
- imageio *
- matplotlib *
- numpy ==1.23.5
- pandas ==1.5.3
- pyshp *
- rasterio *
- scipy ==1.10.1