SatvikPraveen/JAX-NSL

Comprehensive JAX implementation of neural networks and scientific computing. Features distributed training, physics-informed networks, custom autodiff, and advanced optimization. Production-ready code with numerical stability, multi-device parallelism, and research-grade implementations.

23
/ 100
Experimental
No Package No Dependents
Maintenance 13 / 25
Adoption 1 / 25
Maturity 9 / 25
Community 0 / 25

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1

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Language

Jupyter Notebook

License

MIT

Category

transformer-frameworks-wrappers

Last pushed

Mar 09, 2026

Commits (30d)

0

GitHub
Transformer Frameworks Wrappers · 80 models

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