Democratizing uncertainty quantification

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

doi:10.1016/j.jcp.2024.113542

Democratizing uncertainty quantification

Journal Article · Wed Oct 30 00:00:00 EDT 2024 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2024.113542· OSTI ID:2584646

^[1]; Reinarz, Anne ^[2]; Lykkegaard, Mikkel B. ^[3]; Akers, Robert ^[4]; Alghamdi, Amal M.A. ^[5]; Aristoff, David ^[6]; Bangerth, Wolfgang ^[6]; Bénézech, Jean ^[7]; Diez, Matteo ^[8]; Frey, Kurt ^[9]; ^[10]; Jørgensen, Jakob S. ^[5]; Kim, Ki-Tae ^[11]; Kent, Benjamin M. ^[12]; Martinelli, Massimiliano ^[12]; Parno, Matthew ^[13]; Pellegrini, Riccardo ^[8]; Petra, Noemi ^[11]; Riis, Nicolai A. B. ^[14]; Rosenfeld, Katherine ^[9] more »

Karlsruhe Inst. of Technology (KIT) (Germany)
Durham Univ. (United Kingdom)
digiLab, Exeter (United Kingdom)
UK Atomic Energy Authority (UKAEA), Culham (United Kingdom)
Technical Univ. of Denmark, Lyngby (Denmark)
Colorado State Univ., Fort Collins, CO (United States)
Univ. of Bath (United Kingdom)
National Research Council (CNR), Rome (Italy)
Bill & Melinda Gates Foundation (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of California, Merced, CA (United States)
National Research Council (CNR), Pavia (Italy)
Solea Energy, Thetford, VT (United States)
Copenhagen Imaging ApS (Denmark); Technical Univ. of Denmark, Lyngby (Denmark)
Italian National Research Council (INRC), Rome (Italy)
Univ. of Texas, Austin, TX (United States)
Heidelberg Univ. (Germany)

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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: Advanced Scientific Computing Research; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: NA0003525

OSTI ID:: 2584646

Alternate ID(s):: OSTI ID: 2585546

Report Number(s):: SAND2025-08295J

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 521; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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