Enabling UQ for complex modelling workflows

Ma̷lgorzata J. Zimoń, Samuel Antão, Robert Sawko, Alex Skillen, Vadim Elisseev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The increase of computing capabilities promises to address many scientific and engineering problems by enabling simulations to reach new levels of accuracy and scale. The field of uncertainty quantification (UQ) has recently been receiving an increasing amount of attention as it enables reliability study of modelled systems. However, performance of UQ analysis for high-fidelity simulations remains challenging due to exceedingly high complexity of computational workflows. In this paper, we present a UQ study on a complex workflow targeting a thermally stratified flow. We discuss different models that can be used to enable it. We then propose an abstraction at the level of the workflow specification that enables the modeller to quickly switch between UQ models and manage underlying compute infrastructure in a completely transparent way. We show that we can keep the workflow description almost unchanged while benefitting of all the insight the UQ study provides.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2019 - 19th International Conference, 2019, Proceedings
EditorsJoão M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot, Jack J. Dongarra
PublisherSpringer Nature
Number of pages13
ISBN (Print)9783030227494
Publication statusPublished - 8 Jun 2019
Event19th International Conference on Computational Science - Faro, Portugal
Duration: 12 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11540 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computational Science
Abbreviated titleICCS 2019


  • High-performance computing
  • Modelling
  • Uncertainty quantification
  • Workflows


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