tensorial
Library for machine learning on physical tensors
Tensorial is an open-source Python library for machine learning on physical tensors. It combines JAX-based numerical tooling with geometric and equivariant model components for scientific ML, with a focus on atomistic and materials applications.
The project is built for tensor-valued representations and symmetry-aware learning, making it useful for tasks such as molecular and materials modeling, graph-based neural networks, and other physics-informed machine learning workflows.
The project is licensed under the GNU Affero General Public License v3 or later (AGPL-3.0). The project is intended to remain open source, and contributors should be aware of this AGPLv3 licensing model.
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Release files for tensorial 0.6.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tensorial-0.6.5.tar.gz | 100.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tensorial-0.6.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 230.3 kB
Release files / tensorial-0.6.5.tar.gz
| Download URL | tensorial-0.6.5.tar.gz |
|---|---|
| Size | 100.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/7.0.0 CPython/3.12.14
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Release files / tensorial-0.6.5-py3-none-any.whl
| Download URL | tensorial-0.6.5-py3-none-any.whl |
|---|---|
| Size | 129.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.14
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