1) Monte Carlo UQ

A simple example from our README.md:

from queens.distributions import Beta, Normal, Uniform
from queens.drivers import Function
from queens.global_settings import GlobalSettings
from queens.iterators import MonteCarlo
from queens.main import run_iterator
from queens.models import Simulation
from queens.parameters import Parameters
from queens.schedulers import Local

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

    with global_settings:
        # Set up the uncertain parameters
        x1 = Uniform(lower_bound=-3.14, upper_bound=3.14)
        x2 = Normal(mean=0.0, covariance=1.0)
        x3 = Beta(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 = Function(parameters=parameters, function="ishigami90")
        scheduler = Local(
            experiment_name=global_settings.experiment_name, num_jobs=2, num_procs=4
        )
        model = Simulation(scheduler=scheduler, driver=driver)

        # Set up the algorithm
        iterator = MonteCarlo(
            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)

Resulting in the histogram: