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 D.; 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

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.

Citation

Seelinger, L., Reinarz, A., Lykkegaard, M. B., Akers, R., Alghamdi, A. M., 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., Rosenfeld, K., …Scheichl, R. (2025). Democratizing Uncertainty Quantification. Journal of Computational Physics, 521(1), Article 113542. https://doi.org/10.1016/j.jcp.2024.113542

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2024
• Online Publication Date: Nov 7, 2024
• Publication Date: Jan 15, 2025
• Deposit Date: Nov 7, 2024
• Publicly Available Date: Nov 7, 2024
• Journal: Journal of Computational Physics
• Print ISSN: 0021-9991
• Electronic ISSN: 1090-2716
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 521
• Issue: 1
• Article Number: 113542
• DOI: https://doi.org/10.1016/j.jcp.2024.113542
• Public URL: https://durham-repository.worktribe.com/output/2994533