EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing

Robin A. Richardson; David W. Wright; Wouter Edeling; Vytautas Jancauskas; Jalal Lakhlili; Peter V. Coveney

doi:10.5334/jors.303

Abstract

EasyVVUQ is an open source Python library (https://github.com/UCL-CCS/EasyVVUQ) designed to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations. The goal of EasyVVUQ is to make it as easy as possible to implement advanced VVUQ techniques for existing application codes or workflows. Our aim is to expose these features in an accessible way for users of scientific software, in particular for simulation codes running on high performance computers.

Funding statement: We acknowledge funding support from the European Union’s Horizon 2020 research and innovation programme under grant agreement 800925 (VECMA project, www.vecma.eu) and the UK Consortium on Mesoscale Engineering Sciences (UK-COMES, http://www.ukcomes.org), EPSRC reference EP/L00030X/1.

References

Baker, M 2016 1,500 scientists lift the lid on reproducibility. Nature, 533: 452–4. DOI: 10.1038/533452a
Open DOI Search in Google Scholar Back to article
Baudin, M, Dutfoy, A, Iooss, B and Popelin, A-L 2017 OpenTURNS: An In dustrial Software for Uncertainty Quantification in Simulation. In: ‘Handbook of Uncertainty Quantification’, 2001–2038. Springer International Publishing. DOI: 10.1007/978-3-319-12385-1_64
Open DOI Search in Google Scholar Back to article
Debusschere, B, Sargsyan, K, Safta, C and Chowdhary, K 2017 Uncertainty Quantification Toolkit (UQTk). In: ‘Handbook of Uncertainty Quantification’, 1807–1827. Springer International Publishing. DOI: 10.1007/978-3-319-12385-1_56
Open DOI Search in Google Scholar Back to article
Feinberg, J and Langtangen, H P 2015 ‘Chaospy: An open source tool for designing methods of uncertainty quantification’. Journal of Computational Science, 11: 46–57. DOI: 10.1016/j.jocs.2015.08.008
Open DOI Search in Google Scholar Back to article
Gaudier, F 2010 ‘URANIE: The CEA/DEN Uncertainty and Sensitivity platform’. Procedia – Social and Behavioral Sciences, 2(6): 7660–7661. DOI: 10.1016/j.sbspro.2010.05.166
Open DOI Search in Google Scholar Back to article
Groen, D, Richardson, R A, Wright, D W, Jancauskas, V, Sinclair, R, Karlshoefer, P, Vassaux, M, Arabnejad, H, Piontek, T, Kopta, P, Bosak, B, Lakhlili, J, Hoenen, O, Suleimenova, D, Edeling, W, Crommelin, D, Nikishova, A and Coveney, P V 2019 Introducing VECMAtk – Verification Validation and Uncertainty Quantification for Multiscale and HPC Simulations. In: ‘Lecture Notes in Computer Science’, 479–492. Springer International Publishing. DOI: 10.1007/978-3-030-22747-0_36
Open DOI Search in Google Scholar Back to article
Herman, J and Usher, W 2017 ‘SALib: An open-source Python library for Sensitivity Analysis’. The Journal of Open Source Software, 2(9): 97. DOI: 10.21105/joss.00097
Open DOI Search in Google Scholar Back to article
Marelli, S and Sudret, B 2014 UQLab: A Framework for Uncertainty Quantification in Matlab, in ‘Vulnerability Uncertainty, and Risk’. American Society of Civil Engineers. DOI: 10.1061/9780784413609.257
Open DOI Search in Google Scholar Back to article
Myers, J and Copeland, R 2015 Essential SQLAlchemy: Mapping Python to Databases. O’Reilly Media, Inc.
Search in Google Scholar Back to article
Nikishova, A and Hoekstra, A G 2019 ‘Semi-intrusive uncertainty propagation for multiscale models’. Journal of Computational Science, 35: 80–90. DOI: 10.1016/j.jocs.2019.06.007
Open DOI Search in Google Scholar Back to article
Oberkampf, W L, DeLand, S M, Rutherford, B M, Diegert, K V and Alvin, K F 2002 ‘Error and uncertainty in modeling and simulation’. Reliability Engineering & System Safety, 75(3): 333–357. DOI: 10.1016/S0951-8320(01)00120-X
Open DOI Search in Google Scholar Back to article
Oberkampf, W L and Roy, C J 2010 Verification and Validation in Scientific Computing. Cambridge University Press. DOI: 10.1017/CBO9780511760396
Open DOI Search in Google Scholar Back to article
Peng, R 2015 ‘The reproducibility crisis in science: A statistical counterattack’. Significance, 12(3): 30–32. DOI: 10.1111/j.1740-9713.2015.00827.x
Open DOI Search in Google Scholar Back to article
Richardson, R A, Wright, D W, Jancauskas, V, Lakhlili, J and Edeling, W 2019 ‘Easyvvuq v0.5’. Documentation at https://easyvvuq.readthedocs.io. DOI: 10.5281/zenodo.3722092
Open DOI Search in Google Scholar Back to article
Roy, C J and Oberkampf, W L 2011 ‘A comprehensive framework for verification validation, and uncertainty quantification in scientific computing’. Computer Methods in Applied Mechanics and Engineering, 200(25–28): 2131–2144. DOI: 10.1016/j.cma.2011.03.016
Open DOI Search in Google Scholar Back to article
Rubinstein, R Y and Kroese, D P 2007 Simulation and the Monte Carlo Method. John Wiley & Sons Inc. DOI: 10.1002/9780470230381
Open DOI Search in Google Scholar Back to article
Saltelli, A, Ratto, M, Andres, T, Campolongo, F, Cariboni, J, Gatelli, D, Saisana, M and Tarantola, S 2008 Global sensitivity analysis: the primer. John Wiley & Sons. DOI: 10.1002/9780470725184
Open DOI Search in Google Scholar Back to article
Sobol, I M 1998 ‘On quasi-Monte Carlo integrations’. Mathematics and Computers in Simulation, 47(2–5): 103–112. DOI: 10.1016/S0378-4754(98)00096-2
Open DOI Search in Google Scholar Back to article
Tennøe, S, Halnes, G and Einevoll, G T 2018 Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience. Frontiers in Neuroinformatics, 12. DOI: 10.3389/fninf.2018.00049
Open DOI Search in Google Scholar Back to article

EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing

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