VECMAtk

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

Journal Article (2021)
Author(s)

D. Groen (University College London, Brunel University)

H. Arabnejad (Brunel University)

V. Jancauskas (Leibniz Supercomputing Centre)

W. N. Edeling (Centrum Wiskunde & Informatica (CWI))

F. Jansson (Centrum Wiskunde & Informatica (CWI), TU Delft - Atmospheric Remote Sensing)

R. A. Richardson (Netherlands eScience Center, University College London)

J. Lakhlili (EURATOM Association)

L. Veen (Netherlands eScience Center)

B. Bosak (Poznań Supercomputing and Networking Center)

undefined More Authors (External organisation)

Research Group
Atmospheric Remote Sensing
DOI related publication
https://doi.org/10.1098/rsta.2020.0221
More Info
expand_more
Publication Year
2021
Language
English
Research Group
Atmospheric Remote Sensing
Issue number
2197
Volume number
379
Article number
20200221
Pages (from-to)
1-22
Downloads counter
327
Collections
Institutional Repository
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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'.