# Introduction

```{image} images/queens_logo_day.svg
:class: only-light
:width: 500px
:align: center
```

```{image} images/queens_logo_night.svg
:class: only-dark
:width: 500px
:align: center
```


QUEENS (**Q**uantification of **U**ncertain **E**ffects in **En**gineering **S**ystems) is a Python framework for solver-independent multi-query analyses of large-scale computational models.


## Capabilities

 **QUEENS** offers a large collection of cutting-edge algorithms for deterministic and probabilistic analyses such as:
* parameter studies and identification
* sensitivity analysis
* surrogate modeling
* uncertainty quantification
* Bayesian inverse analysis

 **QUEENS** provides a modular architecture for:
* parallel queries of large-scale computational models
* robust data, resource, and error management
* easy switching between analysis types
* smooth scaling from laptop to HPC cluster



## Installation

>**Prerequisites**: Unix system and environment management system (we recommend [miniforge](https://conda-forge.org/download/))



Clone the QUEENS repository to your local machine. Navigate to its base directory, then:
```
conda env create
conda activate queens
pip install -e .
```


For development, install the additional required packages via:

```
pip install -e .[develop]
```


> Note: We recommend using conda/mamba environments and installing performance-critical packages (e.g., numpy, scipy, ...) using `conda install <packagename>.` The reason for this is the choice of BLAS library (linear algebra packages). Conda (depending on the channel) installs numpy and the [mkl](https://www.intel.com/content/www/us/en/developer/articles/technical/intel-mkl-and-third-party-applications-how-to-use-them-together.html) library from Intel, in contrast to pip which defaults back to the linear algebra package installed on the system. According to certain benchmarks ([here](http://markus-beuckelmann.de/blog/boosting-numpy-blas.html) or [here](https://medium.com/analytics-vidhya/why-conda-install-instead-of-pip-install-ba4c6826a0ae)), the mkl library is able to outperform other linear algebra libraries, especially on Intel devices. Particularly for use cases where linear algebra operations dominate the computational costs, the benefit can be huge.

To test for a successful installation, run the test suite:
```
pytest -n <number_of_workers>
```
Consult the [documentation of QUEENS tests](https://queens-py.github.io/queens/testing.html) for more details on testing.

## Citing QUEENS
You used QUEENS for a publication? Don't forget to cite

```bib
@misc{queens,
  author       = {QUEENS},
  title        = {QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models},
  year         = {2025},
  howpublished = {\url{https://www.queens-py.org}}
}
```

and the respective methods papers.

## License

Licensed under GNU LGPL-3.0 (or later). See [LICENSE](https://www.github.com/queens-py/queens/blob/main/LICENSE).