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

Department of Mechanical Engineering

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

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
Early online date	30 Oct 2024
DOIs	https://doi.org/10.1016/j.jcp.2024.113542
Publication status	Published - 15 Jan 2025

Data Availability Statement

Simulation codes and relevant data for inference problems are published as container images in the online benchmark library.

Funding

D. Aristoff gratefully acknowledges support from the National Science Foundation via awards DMS-1818726 and DMS-2111277. W. Bangerth was partially supported by the National Science Foundation under award OAC-1835673 as part of the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program; by award DMS-1821210; and by award EAR-1925595. J. Bénézech research was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through the Programme Grant EP/S017038/1 “Certification of Design: Reshaping the Testing Pyramid” and made use of the Hamilton HPC Service of Durham University. The code is developed and integrated in the Distributed and Unified Numerics Environment (DUNE) https://www.dune-project.org/. J. D. Jakeman was supported by the US Department of Energy's Office of Advanced Scientific Computing Research program. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC (NTESS), a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. This written work is authored by an employee of NTESS. The employee, not NTESS, owns the right, title and interest in and to the written work and is responsible for its contents. Any subjective views or opinions that might be expressed in the written work do not necessarily represent the views of the U.S. Government. The publisher acknowledges that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this written work or allow others to do so, for U.S. Government purposes. The DOE will provide public access to results of federally sponsored research in accordance with the DOE Public Access Plan. A. Reinarz and L. Seelinger gratefully acknowledge support by Cristian Mezzanotte (Google). This material is based upon work supported by the Google Cloud Research Credits program with the award GCP211124249. The work of N. A. B. Riis, J. S. Jørgensen and A.M.A. Alghamdi was supported by The Villum Foundation (grant no. 25893). R. Scheichl and L. Seelinger gratefully acknowledge support by the state of Baden-Württemberg through bwHPC, as well as the German Research Foundation (DFG) through grant INST 35/1597-1 FUGG, as well as its Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster). L. Tamellini has been partially supported by the project 202222PACR “Numerical approximation of uncertainty quantification problems for PDEs by multi-fidelity methods (UQ-FLY)”, funded by European Union—NextGenerationEU. Lorenzo Tamellini and Massimiliano Martinelli are members of the Gruppo Nazionale Calcolo Scientifico-Istituto Nazionale di Alta Matematica (GNCS-INdAM), and have been partially supported by the ICSC—Centro Nazionale di Ricerca in High Performance Computing, Big Data, and Quantum Computing, funded by European Union—NextGenerationEU. M. Diez, R. Pellegrini, and A. Serani are partially supported by The Office of Naval Research through NICOP grant N62909-21-1-2042, administered by Woei-Min Lin, Elena McCarthy, and Salahuddin Ahmed of the Office of Naval Research and Office of Naval Research Global, and their work has been conducted in collaboration with the NATO task group AVT-331 on “Goal-driven, multi-fidelity approaches for military vehicle system-level design”.

Keywords

Benchmarks
High-performance computing
Numerical simulation
Scientific software
Uncertainty quantification

ASJC Scopus subject areas

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

Access to Document

10.1016/j.jcp.2024.113542Licence: CC BY

Cite this

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

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

@article{f0337d9aa0644893bc8e8ea37fcb057a,

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",

year = "2025",

month = jan,

day = "15",

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

language = "English",

volume = "521",

journal = "Journal of Computational Physics",

issn = "0021-9991",

publisher = "Elsevier Academic Press Inc",

}

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 - 2025/1/15

Y1 - 2025/1/15

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

UR - http://www.scopus.com/inward/record.url?scp=85208173240&partnerID=8YFLogxK

U2 - 10.1016/j.jcp.2024.113542

DO - 10.1016/j.jcp.2024.113542

M3 - 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

Abstract

Data Availability Statement

Funding

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this