ScimBa
Scimba is a Python library that implements several Scientific Machine Learning (SciML) methods for PDE problems, as well as tools for hybrid numerical methods.
The current version of the code solves parametric PDEs using various nonlinear approximation spaces such as neural networks, low-rank approximations, and nonlinear kernel methods. These methods:
- can handle complex geometries generated via level-set techniques and mappings, including sub-volumetric and surface domains;
- support function projections as well as elliptic, time-dependent, and kinetic parametric PDEs;
- are compatible with both space–time algorithms (PINN, Deep Ritz) and time-sequential ones (discrete PINNs, neural Galerkin and neural semi-Lagrangian schemes).
To achieve this, the code provides several optimization strategies, including:
- Adam, L-BFGS, SS-BFGS and SS-Broyden;
- natural gradient methods (for neural network-based models);
- hybrid least-squares approaches.
There are two versions of Scimba based on two backends:
scimba_torchis based onPyTorchscimba_jaxis based onJaxand is still released as an experimental module. There is no warranty concerning the compatibility of the current release ofscimba_jaxwith future versions. The given test coverage does not concernscimba_jax.
Documentation: https://www.scimba.org/
Code repository: https://gitlab.com/scimba/scimba/
Development guide: https://www.scimba.org/#development
Release files for scimba 1.3.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scimba-1.3.4.tar.gz | 379.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scimba-1.3.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 902.2 kB
Release files / scimba-1.3.4.tar.gz
| Download URL | scimba-1.3.4.tar.gz |
|---|---|
| Size | 379.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/6.2.0 CPython/3.13.13
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Release files / scimba-1.3.4-py3-none-any.whl
| Download URL | scimba-1.3.4-py3-none-any.whl |
|---|---|
| Size | 522.4 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 |
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twine/6.2.0 CPython/3.13.13
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