Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 118468 |
| Fachzeitschrift | Computer Methods in Applied Mechanics and Engineering |
| Jahrgang | 449 |
| Frühes Online-Datum | 6 Nov. 2025 |
| Publikationsstatus | Veröffentlicht - 1 Feb. 2026 |
Abstract
This article presents an efficient multiphysics framework for linear and nonlinear stochastic poroelastic analysis. Specifically, an uncertainty decoupling method is developed to decouple the overall multiphysics stochastic solution into stochastic solutions of each physical field. Stochastic solutions of each physical field are approximated by a unified representation and expressed as the summation of a series of products of triplets of spatial functions, temporal functions and random variables. Each triplet is then solved in a greedy way using a dedicated iteration, which transforms the original stochastic problems into deterministic problems and very low-dimensional stochastic problems that can be solved almost independently of stochastic dimensions. Compared with existing coupled stochastic solution approximations, the proposed uncertainty decoupling method has better convergence without loss of accuracy, thus saving computational efforts. Numerical examples of linear and nonlinear stochastic poroelasticity with high-dimensional random inputs demonstrate the promising performance of the proposed framework.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Numerische Mechanik
- Ingenieurwesen (insg.)
- Werkstoffmechanik
- Ingenieurwesen (insg.)
- Maschinenbau
- Physik und Astronomie (insg.)
- Allgemeine Physik und Astronomie
- Informatik (insg.)
- Angewandte Informatik
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in: Computer Methods in Applied Mechanics and Engineering, Jahrgang 449, 118468, 01.02.2026.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Multiphysics uncertainty decoupling methods for stochastic poroelastic analysis
AU - Zheng, Zhibao
AU - Néron, David
AU - Nackenhorst, Udo
N1 - Publisher Copyright: © 2025 The Author(s)
PY - 2026/2/1
Y1 - 2026/2/1
N2 - This article presents an efficient multiphysics framework for linear and nonlinear stochastic poroelastic analysis. Specifically, an uncertainty decoupling method is developed to decouple the overall multiphysics stochastic solution into stochastic solutions of each physical field. Stochastic solutions of each physical field are approximated by a unified representation and expressed as the summation of a series of products of triplets of spatial functions, temporal functions and random variables. Each triplet is then solved in a greedy way using a dedicated iteration, which transforms the original stochastic problems into deterministic problems and very low-dimensional stochastic problems that can be solved almost independently of stochastic dimensions. Compared with existing coupled stochastic solution approximations, the proposed uncertainty decoupling method has better convergence without loss of accuracy, thus saving computational efforts. Numerical examples of linear and nonlinear stochastic poroelasticity with high-dimensional random inputs demonstrate the promising performance of the proposed framework.
AB - This article presents an efficient multiphysics framework for linear and nonlinear stochastic poroelastic analysis. Specifically, an uncertainty decoupling method is developed to decouple the overall multiphysics stochastic solution into stochastic solutions of each physical field. Stochastic solutions of each physical field are approximated by a unified representation and expressed as the summation of a series of products of triplets of spatial functions, temporal functions and random variables. Each triplet is then solved in a greedy way using a dedicated iteration, which transforms the original stochastic problems into deterministic problems and very low-dimensional stochastic problems that can be solved almost independently of stochastic dimensions. Compared with existing coupled stochastic solution approximations, the proposed uncertainty decoupling method has better convergence without loss of accuracy, thus saving computational efforts. Numerical examples of linear and nonlinear stochastic poroelasticity with high-dimensional random inputs demonstrate the promising performance of the proposed framework.
KW - Multiphysics uncertainty decoupling
KW - Stochastic model order reduction
KW - Stochastic multiphysics problems
KW - Stochastic poroelasticity
UR - http://www.scopus.com/inward/record.url?scp=105020827931&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2025.118468
DO - 10.1016/j.cma.2025.118468
M3 - Article
AN - SCOPUS:105020827931
VL - 449
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 118468
ER -