QUEENS (Quantification of Uncertain Effects in Engineering Systems) is a Python framework for solver-independent multi-query analyses of large-scale computational models.

📈 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

🌐 Website: queens-py.org

📖 Documentation: queens-py.github.io/queens

🚀 Getting started

Prerequisites: Unix system and environment management system (we recommend miniforge)

Clone the QUEENS repository to your local machine. Navigate to its base directory, then:

conda env create
conda activate queens
pip install -e .

👑 Workflow example

Let's consider a parallelized Monte Carlo simulation of the Ishigami function:

from queens.distributions import BetaDistribution, NormalDistribution, UniformDistribution
from queens.drivers import FunctionDriver
from queens.global_settings import GlobalSettings
from queens.iterators import MonteCarloIterator
from queens.main import run_iterator
from queens.models import SimulationModel
from queens.parameters import Parameters
from queens.schedulers import LocalScheduler

if __name__ == "__main__":
    # Set up the global settings
    global_settings = GlobalSettings(experiment_name="monte_carlo_uq", output_dir=".")

    # Set up the uncertain parameters
    x1 = UniformDistribution(lower_bound=-3.14, upper_bound=3.14)
    x2 = NormalDistribution(mean=0.0, covariance=1.0)
    x3 = BetaDistribution(lower_bound=-3.14, upper_bound=3.14, a=2.0, b=5.0)
    parameters = Parameters(x1=x1, x2=x2, x3=x3)

    # Set up the model
    driver = FunctionDriver(parameters=parameters, function="ishigami90")
    scheduler = LocalScheduler(
        experiment_name=global_settings.experiment_name, num_jobs=2, num_procs=4
    )
    model = SimulationModel(scheduler=scheduler, driver=driver)

    # Set up the algorithm
    iterator = MonteCarloIterator(
        model=model,
        parameters=parameters,
        global_settings=global_settings,
        seed=42,
        num_samples=1000,
        result_description={"write_results": True, "plot_results": True},
    )

    # Start QUEENS run
    run_iterator(iterator, global_settings=global_settings)

👥 Contributing

Your contributions are welcome! Please follow our contributing guidelines and code of conduct.

📃 How to cite

If you use QUEENS in your work, please cite the QUEENS paper and relevant method papers.

@inproceedings{queens,
  title={QUEENS: An Open-Source Python Framework for Solver-
Independent Analyses of Large-Scale Computational Models},
  author={Qommunity},
  pages={1--2},
  year={2025}
}

👩‍⚖️ License

Licensed under GNU LGPL-3.0 (or later). See LICENSE.