Abstract
Methods of parametric order reduction are very appealing for solving parameter-dependent models at the fields level, because they provide fast simulations and low systematic error. This paper presents a self-adaptive framework for computing reduced-order models featuring affine and non-affine parameters. It is based on a hypercube partitioning of the domain of non-affine parameters and employs non-uniform grid refinement, controlled by a suitable error indicator. Compared with state-of-the-art entire-domain methods, the proposed sub-domain approach achieves significant improvements in memory consumption and computer run time.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
