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The aim of EasyVVUQ is to facilitate verification, validation anduncertainty quantification (VVUQ) for a wide variety ofsimulations. While very convenient for simple cases, EasyVVUQ is particularly well suited in situations where the simulations are computationally expensive,heterogeneous computing resources are necessary, the sampling space is very large or book-keeping is prohibitivelycomplex. It coordinates execution using an efficient database, it is fault tolerant and all progress can be saved.

Here are some examples of questions EasyVVUQ can answer about your code:

• Given the uncertainties in input parameters, what is the distribution of the output?
• What percentage of the output variance each input parameter contributes?

It also lets you construct surrogate models that are cheaper to evaluate than the complete simulation.

The high-level overview of the library is avalable at ourreadthedocs.

For the quick start with EasyVVUQ we reccommend to check our basic interactive tutorial availablehere.

Available analysis and sampling methods:

• Polynomial Chaos Expansion
• Stochastic Collocation
• Dimension-adaptive Stochastic Collocation for high-dimensional inputs (incl notebook in./tutorials andtheoretical tutorial)
• Simplex Stochastic Collocation for irregular outputs (incl notebook in./tutorials andarticle)
• Monte Carlo Sensitivity Analysis
• Markov-Chain Monte Carlo

EasyVVUQ also supports building surrogate models using:

• Polynomial Chaos Expansion
• Stochastic Collocation
• Gaussian Processes

Supported computing resources:

• Traditional clusters
• Kubernetes clusters

The easiest way to get familiar with the provided functionality is to follow the tutorials (*.ipynb files) in ourBinder.

To use the library you will need Python 3.7+.

If you are unsure of the version of python your defaultpip works for type:

pip --version

If the output ends with(python 2.7) you should replacepip withpip3 in the following commands.

The following should fully install the library:

pip install easyvvuq

To upgrade the library use:

pip install easyvvuq --upgrade

Alternatively, you can manually install EasyVVUQ.First clone the repository to your computer:

git clone https://github.com/UCL-CCS/EasyVVUQ.git

Note: As above, you need to be sure you are installing for Python 3 - if necessary replacepip withpip3 andpython withpython3 in the commands below.

We are trying to keep dependencies at a minimum but a few are inevitable, to install these, install the EasyVVUQ library itself and build a test case use:

cd EasyVVUQ/bash install_EasyVVUQ.sh

You can find the EasyVVUQ API documentation on ourGitHub Pages.

Richardson, R A, Wright, D W, Edeling, W, Jancauskas, V, Lakhlili, J and Coveney, P V.2020 EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing.Journal of Open Research Software, 8: 11.DOI: 10.5334/jors.303.

Wright, D.W., Richardson, R.A., Edeling, W., Lakhlili, J., Sinclair, R.C., Jancauskas, V., Suleimenova, D., Bosak, B., Kulczewski, M., Piontek, T., Kopta, P., Chirca, I., Arabnejad, H., Luk, O.O., Hoenen, O., Weglarz, J., Crommelin, D., Groen, D. and Coveney, P.V. (2020), Building Confidence in Simulation: Applications of EasyVVUQ. Adv. Theory Simul., 3: 1900246.DOI: 10.1002/adts.201900246.

Development was funded by the EU Horizon 2020 projectVECMA.