Adaptive Reduced-Basis Generation for Reduced-Order Modeling for the Solution of Stochastic Nondestructive Evaluation Problems
- Submitting institution
-
University of Durham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 104099
- Type
- D - Journal article
- DOI
-
10.1016/j.cma.2016.06.024
- Title of journal
- Computer Methods in Applied Mechanics and Engineering
- Article number
- -
- First page
- 172
- Volume
- 310
- Issue
- -
- ISSN
- 00457825
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
https://doi.org/10.1016/j.cma.2016.06.024
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a new approach to adaptively explore the solution space of a stochastic partial differential equations, to then create a reduced-order model with sufficient accuracy to aid in the solution of an associated inverse problem. The work highlights the need to move beyond static generation of reduced-order models that is primarily used to ensure that accurate inverse problem solutions can be obtained with reasonable computational efficiency. As such, this work has been a significant contributor to the ongoing change in focus of researchers in stochastic mechanics and reduced-order/surrogate modelling toward adaptive techniques for model creation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
