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

doi:10.1016/j.jcp.2024.113542

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 RosenfeldAndrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl

^*Corresponding author for this work

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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 language	English
Article number	113542
Journal	Journal of Computational Physics
Volume	521
Number of pages	35
ISSN	0021-9991
DOIs	https://doi.org/10.1016/j.jcp.2024.113542
Publication status	Published - 2025

Keywords

Benchmarks
High-performance computing
Numerical simulation
Scientific software
Uncertainty quantification

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Seelinger, L., Reinarz, A., Lykkegaard, M. B., Akers, R., Alghamdi, A. M. A., Aristoff, D., Bangerth, W., Bénézech, J., Diez, M., Frey, K., Jakeman, J. D., Jørgensen, J. S., Kim, K. T., Kent, B. M., Martinelli, M., Parno, M., Pellegrini, R., Petra, N., Riis, N. A. B., ... Scheichl, R. (2025). Democratizing uncertainty quantification. Journal of Computational Physics, 521, Article 113542. https://doi.org/10.1016/j.jcp.2024.113542

@article{d848201f8065404290796551bef8552a,

title = "Democratizing uncertainty quantification",

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.",

keywords = "Benchmarks, High-performance computing, Numerical simulation, Scientific software, Uncertainty quantification",

author = "Linus Seelinger and Anne Reinarz and Lykkegaard, {Mikkel B.} and Robert Akers and Alghamdi, {Amal M.A.} and David Aristoff and Wolfgang Bangerth and Jean B{\'e}n{\'e}zech and Matteo Diez and Kurt Frey and Jakeman, {John D.} and J{\o}rgensen, {Jakob S.} and Kim, {Ki Tae} and Kent, {Benjamin M.} and Massimiliano Martinelli and Matthew Parno and Riccardo Pellegrini and Noemi Petra and Riis, {Nicolai A.B.} and Katherine Rosenfeld and Andrea Serani and Lorenzo Tamellini and Umberto Villa and Dodwell, {Tim J.} and Robert Scheichl",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2025",

doi = "10.1016/j.jcp.2024.113542",

language = "English",

volume = "521",

journal = "Journal of Computational Physics",

issn = "0021-9991",

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Seelinger, L, Reinarz, A, Lykkegaard, MB, Akers, R, Alghamdi, AMA, Aristoff, D, Bangerth, W, Bénézech, J, Diez, M, Frey, K, Jakeman, JD, Jørgensen, JS, Kim, KT, Kent, BM, Martinelli, M, Parno, M, Pellegrini, R, Petra, N, Riis, NAB, Rosenfeld, K, Serani, A, Tamellini, L, Villa, U, Dodwell, TJ & Scheichl, R 2025, 'Democratizing uncertainty quantification', Journal of Computational Physics, vol. 521, 113542. https://doi.org/10.1016/j.jcp.2024.113542

TY - JOUR

T1 - Democratizing uncertainty quantification

AU - Seelinger, Linus

AU - Reinarz, Anne

AU - Lykkegaard, Mikkel B.

AU - Akers, Robert

AU - Alghamdi, Amal M.A.

AU - Aristoff, David

AU - Bangerth, Wolfgang

AU - Bénézech, Jean

AU - Diez, Matteo

AU - Frey, Kurt

AU - Jakeman, John D.

AU - Jørgensen, Jakob S.

AU - Kim, Ki Tae

AU - Kent, Benjamin M.

AU - Martinelli, Massimiliano

AU - Parno, Matthew

AU - Pellegrini, Riccardo

AU - Petra, Noemi

AU - Riis, Nicolai A.B.

AU - Rosenfeld, Katherine

AU - Serani, Andrea

AU - Tamellini, Lorenzo

AU - Villa, Umberto

AU - Dodwell, Tim J.

AU - Scheichl, Robert

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Benchmarks

KW - High-performance computing

KW - Numerical simulation

KW - Scientific software

KW - Uncertainty quantification

U2 - 10.1016/j.jcp.2024.113542

DO - 10.1016/j.jcp.2024.113542

M3 - Journal article

AN - SCOPUS:85208173240

SN - 0021-9991

VL - 521

JO - Journal of Computational Physics

JF - Journal of Computational Physics

M1 - 113542

ER -

Democratizing uncertainty quantification

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Keywords

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