_version_ 1866914942400069632
author 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
author_facet 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
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2402_13768
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle 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
Mathematical Software
Applications
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.
title Democratizing Uncertainty Quantification
topic Mathematical Software
Applications
url https://arxiv.org/abs/2402.13768