Metadata-Version: 2.4
Name: uqdeepnn
Version: 0.1.20
Summary: PyTorch uncertainty quantification toolkit with deep ensembles, Bayes-by-Backprop VI, Laplace, SGLD, MC Dropout, Gaussian Processes, and scientific ML backbones.
Author: Vispi Nevile Karkaria, Contributors
License: MIT
Project-URL: Homepage, https://github.com/Vispikarkaria/Deep-UQ
Project-URL: Repository, https://github.com/Vispikarkaria/Deep-UQ
Project-URL: Documentation, https://vispikarkaria.github.io/Deep-UQ/
Keywords: uncertainty-quantification,bayesian,deep-learning,pytorch,variational-inference,mcmc,laplace,dropout
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: tqdm
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scikit-learn
Provides-Extra: dev
Requires-Dist: black<25,>=24; extra == "dev"
Requires-Dist: mypy<2,>=1.11; extra == "dev"
Requires-Dist: pre-commit<4,>=3.8; extra == "dev"
Requires-Dist: ruff<1,>=0.6.9; extra == "dev"
Provides-Extra: tests
Requires-Dist: pytest<9,>=8; extra == "tests"
Requires-Dist: pytest-cov<6,>=5; extra == "tests"
Provides-Extra: docs
Requires-Dist: mkdocs<2,>=1.6; extra == "docs"
Requires-Dist: mkdocs-git-revision-date-localized-plugin<2,>=1.2; extra == "docs"
Requires-Dist: mkdocs-jupyter<1,>=0.24; extra == "docs"
Requires-Dist: mkdocs-material<10,>=9.5; extra == "docs"
Requires-Dist: mkdocs-minify-plugin<1,>=0.8; extra == "docs"
Requires-Dist: mkdocs-section-index<1,>=0.3; extra == "docs"
Requires-Dist: mkdocstrings[python]<1,>=0.25; extra == "docs"
Provides-Extra: benchmarks
Requires-Dist: pandas<3,>=2.0; extra == "benchmarks"
Provides-Extra: notebooks
Requires-Dist: ipykernel<7,>=6; extra == "notebooks"
Requires-Dist: jupyter<2,>=1.0; extra == "notebooks"
Dynamic: license-file

Deep-UQ is a PyTorch toolkit for uncertainty-aware machine learning.

It collects practical uncertainty quantification methods, Gaussian-process models,
and scientific machine learning backbones in one package, with a common focus on
predictive uncertainty for regression, classification, and field-to-field surrogate modeling.

## Install

```bash
pip install uqdeepnn
```

For the legacy `kron` and `full` Laplace backends used in older Deep-UQ
tutorials, install the optional Laplace extra:

```bash
pip install "uqdeepnn[laplace]"
```

## Import

```python
import deepuq
```

## Included methods

- Deep Ensembles
- Variational Inference (Bayes by Backprop, heteroscedastic VI, multi-output VI, and last-layer VI)
- Laplace Approximation
- MCMC via SGLD
- MC Dropout
- Gaussian Processes

## Scientific machine learning backbones

- DeepONet
- Fourier Neural Operator (FNO)
- Graph Neural Operators
- CNN / ResNet spatial surrogates
- U-Net backbones
- Physics-Informed Neural Networks (PINNs)

## Included data utilities and examples

- The Well Gray-Scott loader for graph-operator tutorials
- Scientific notebooks for operators, graph models, ensembles, VI, PINNs, diffusion, and Laplace UQ

## Documentation

- Docs: https://vispikarkaria.github.io/Deep-UQ/
- Tutorials: https://vispikarkaria.github.io/Deep-UQ/tutorials/
- API reference: https://vispikarkaria.github.io/Deep-UQ/api/
- Variational Inference guide: https://vispikarkaria.github.io/Deep-UQ/methods/variational-inference/
- GitHub: https://github.com/Vispikarkaria/Deep-UQ

## Package names

- PyPI package: `uqdeepnn`
- Python import: `deepuq`
- Project / docs name: `Deep-UQ`
