NVIDIA/physicsnemo
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 54 days ago, with 102 contributors on GitHub.
Use Cases & Benefits
- Provides an open-source deep-learning framework for building, training, and fine-tuning physics-informed machine learning models using state-of-the-art SciML methods.
- Delivers scalable, GPU-optimized training pipelines with seamless PyTorch integration and a rich suite of physics-informed model architectures for efficient AI4Science development.
- Use for developing and deploying physics-informed neural networks and neural operators for scientific simulations and engineering problems.
- Use for accelerating computational fluid dynamics (CFD) research by leveraging pretrained models and optimized training on NVIDIA GPUs.
- Use for integrating physics constraints into machine learning workflows to create hybrid models combining data-driven and physics-based approaches.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
📝 NVIDIA Modulus has been renamed to NVIDIA PhysicsNeMo
NVIDIA PhysicsNeMo is an open-source deep-learning framework for building, training, fine-tuning, and inferring Physics AI models using state-of-the-art SciML methods for AI4Science and engineering.
PhysicsNeMo provides Python modules to compose scalable and optimized training and inference pipelines to explore, develop, validate, and deploy AI models that combine physics knowledge with data, enabling real-time predictions.
Whether you are exploring the use of neural operators, GNNs, or transformers, or are interested in Physics-Informed Neural Networks or a hybrid approach in between, PhysicsNeMo provides you with an optimized stack that will enable you to train your models at scale.
At a granular level, PhysicsNeMo is developed as modular functionality and therefore provides built-in composable modules that are packaged into a few key components:
For a complete list, refer to the PhysicsNeMo API documentation for PhysicsNeMo.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
831+83
Open issues
116+33
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| onnx/onnx | 21.6K | — | 63 | 31 days ago |
| mlc-ai/web-llm | 19.3K | — | 59 | 16 days ago |
| lukas-blecher/LaTeX-OCR | 16.6K | — | 18 | — |
| milesial/Pytorch-UNet | 11.7K | — | 18 | — |
| NVIDIA/physicsnemo | 3.4K | — | 62 | 54 days ago |
| SharpAI/DeepCamera | 3.1K | — | 31 | 227 days ago |
| Category median | 16.6K | — | 31 | — |
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