Space-Time Reduced-Order Modeling for Uncertainty Quantification

Technical Report · Sun Nov 14 04:00:00 EST 2021

DOI:https://doi.org/10.2172/1830096· OSTI ID:1830096

Jin, Ruhui ^[1]; Rizzi, Francesco ^[2]; Parish, Eric ^[3]

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1. Univ. of Texas, Austin, TX (United States)
2. NexGen Analytics, Sheridan, WY (United States)
3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 1830096
• Report Number(s): SAND2021-14472R; 701555
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING