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Democratizing uncertainty quantification

  • Linus Seelinger*
  • , Anne Reinarz
  • , Mikkel B. Lykkegaard
  • , Robert Akers
  • , Amal M.A. Alghamdi
  • , David Aristoff
  • , Wolfgang Bangerth
  • , Jean Bénézech
  • , Matteo Diez
  • , Kurt Frey
  • , John D. Jakeman
  • , Jakob S. Jørgensen
  • , Ki Tae Kim
  • , Benjamin M. Kent
  • , Massimiliano Martinelli
  • , Matthew Parno
  • , Riccardo Pellegrini
  • , Noemi Petra
  • , Nicolai A.B. Riis
  • , Katherine Rosenfeld
  • Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl
*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.

Original languageEnglish
Article number113542
JournalJournal of Computational Physics
Volume521
DOIs
StatePublished - 15 Jan 2025

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Keywords

  • Benchmarks
  • High-performance computing
  • Numerical simulation
  • Scientific software
  • Uncertainty quantification

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

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