https://github.com/awslabs/ai-surrogate-models-in-engineering-on-aws
https://github.com/awslabs/ai-surrogate-models-in-engineering-on-aws
Science Score: 26.0%
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
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✓.zenodo.json file
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○DOI references
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○Scientific vocabulary similarity
Low similarity (8.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: awslabs
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://awslabs.github.io/ai-surrogate-models-in-engineering-on-aws/
- Size: 13.3 MB
Statistics
- Stars: 12
- Watchers: 3
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
AI Surrogate Models in Engineering on AWS (MLSimKit)
AI Surrogate Models in Engineering on AWS (project name: ML for Simulation Toolkit or MLSimKit) is a Python library that enables engineers to use machine learning models for near real-time predictions of physics-based simulations. By training models on existing simulation data, MLSimKit allows for rapid design iterations without the computational expense of traditional simulations.
Key Features
- KPI Prediction: Predict key performance indicators (e.g., drag coefficient, lift coefficient) directly from geometries
- Slice Prediction: Generate 2D cross-sectional slices of flow fields from 3D geometries
- Surface Variable Prediction: Predict surface properties (e.g., pressure, wall shear stress) on 3D geometries
Installation
```bash
Install from source
git clone https://github.com/awslabs/ai-surrogate-models-in-engineering-on-aws.git cd ai-surrogate-models-in-engineering-on-aws pip install . ```
Documentation
For comprehensive documentation on quickstarts, tutorials, datasets, and other topics, visit: https://awslabs.github.io/ai-surrogate-models-in-engineering-on-aws/
License
This project is licensed under the Apache License 2.0. See the LICENSE file for details.
Owner
- Name: Amazon Web Services - Labs
- Login: awslabs
- Kind: organization
- Location: Seattle, WA
- Website: http://amazon.com/aws/
- Repositories: 914
- Profile: https://github.com/awslabs
AWS Labs
GitHub Events
Total
- Issues event: 2
- Watch event: 9
- Issue comment event: 1
- Public event: 1
- Push event: 5
- Pull request review event: 2
- Pull request event: 3
- Fork event: 1
- Create event: 2
Last Year
- Issues event: 2
- Watch event: 9
- Issue comment event: 1
- Public event: 1
- Push event: 5
- Pull request review event: 2
- Pull request event: 3
- Fork event: 1
- Create event: 2
Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- peaceiris/actions-gh-pages v3 composite
- accelerate >=0.29
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- huggingface-hub >=0.30.2
- jinja2 >=3.1.4
- matplotlib >=3.8
- mlflow >=2.12.2
- myst-parser >=2.0
- numpy ~=1.26
- opencv-python ~=4.9.0
- pandas >=2.1
- pydantic >=2.3.0
- pytest >=8.1
- pytest-cov >=5.0
- pytest-mock >=3.14
- pyvista >=0.41
- pyyaml >=6.0
- ruff >=0.4
- scikit-learn >=1.3
- setuptools_scm >=8.0
- sphinx >=7.3
- torch >=2.1,<2.6
- torch-geometric >=2.4
- torch-summary >=1.4
- torchmetrics >=1.3
- torchvision >=0.16
- tqdm >=4.66.3
- trimesh >=3.23
- vtk >=9.2
- werkzeug >=3.0.3