VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations

Author:

Groen D.12ORCID,Arabnejad H.1ORCID,Jancauskas V.3ORCID,Edeling W. N.4ORCID,Jansson F.45,Richardson R. A.26,Lakhlili J.7,Veen L.6,Bosak B.8,Kopta P.8,Wright D. W.2,Monnier N.9,Karlshoefer P.9,Suleimenova D.1ORCID,Sinclair R.2,Vassaux M.2,Nikishova A.10ORCID,Bieniek M.2ORCID,Luk Onnie O.7ORCID,Kulczewski M.8,Raffin E.9ORCID,Crommelin D.411,Hoenen O.7,Coster D. P.7,Piontek T.8,Coveney P. V.210ORCID

Affiliation:

1. Department of Computer Science, Brunel University London, London, UK

2. Centre for Computational Science, University College London, London, UK

3. Leibniz Supercomputing Centre, Garching, Germany

4. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

5. Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands

6. Netherlands eScience Center, Amsterdam, The Netherlands

7. Max Planck Institute for Plasma Physics - Garching, Munich, Germany

8. Poznań Supercomputing and Networking Center, Poznań, Poland

9. CEPP - Center for Excellence in Performance Programming, Atos Bull, Rennes, France

10. Computational Science Lab, Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands

11. Korteweg-de Vries Institute for Mathematics, Amsterdam, The Netherlands

Abstract

We present the VECMA toolkit (VECMAtk), a flexible software environment for single and multiscale simulations that introduces directly applicable and reusable procedures for verification, validation (V&V), sensitivity analysis (SA) and uncertainty quantication (UQ). It enables users to verify key aspects of their applications, systematically compare and validate the simulation outputs against observational or benchmark data, and run simulations conveniently on any platform from the desktop to current multi-petascale computers. In this sequel to our paper on VECMAtk which we presented last year [ 1 ] we focus on a range of functional and performance improvements that we have introduced, cover newly introduced components, and applications examples from seven different domains such as conflict modelling and environmental sciences. We also present several implemented patterns for UQ/SA and V&V, and guide the reader through one example concerning COVID-19 modelling in detail. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.

Funder

H2020 European Institute of Innovation and Technology

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

Reference67 articles.

1. Groen D et al. 2019 Introducing vecmatk-verification validation and uncertainty quantification for multiscale and hpc simulations. In Int. Conf. on Computational Science Faro Portugal pp. 479–492. Berlin Germany: Springer. (doi:10.1007/978-3-030-22747-0_36)

2. A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing

3. National Research Council of the National Academies. 2012 Assessing the reliability of complex models: Mathematical and statistical foundations of verification validation and uncertainty quantification. National Academies Press. (doi:10.17226/13395)

4. Sensitivity-driven simulation development: a case study in forced migration

5. Schwer LE. 2009 Guide for verification and validation in computational solid mechanics. In the 20th Int. Conf. on Structural Mechanics in Reactor Technology . New York NY: American Society of Mechanical Engineers. See https://repository.lib.ncsu.edu/bitstream/handle/1840.20/23659/3_paper_2010.

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