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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16537 |
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| _version_ | 1866911853221773312 |
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| author | Keil, Tim Ohlberger, Mario Schindler, Felix Schleuß, Julia |
| author_facet | Keil, Tim Ohlberger, Mario Schindler, Felix Schleuß, Julia |
| contents | To efficiently tackle parametrized multi and/or large scale problems, we propose an adaptive localized model order reduction framework combining both local offline training and local online enrichment with localized error control. For the latter, we adapt the residual localization strategy introduced in [Buhr, Engwer, Ohlberger, Rave, SIAM J. Sci. Comput., 2017] which allows to derive a localized a posteriori error estimator that can be employed to adaptively enrich the reduced solution space locally where needed. Numerical experiments demonstrate the potential of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Local training and enrichment based on a residual localization strategy Keil, Tim Ohlberger, Mario Schindler, Felix Schleuß, Julia Numerical Analysis To efficiently tackle parametrized multi and/or large scale problems, we propose an adaptive localized model order reduction framework combining both local offline training and local online enrichment with localized error control. For the latter, we adapt the residual localization strategy introduced in [Buhr, Engwer, Ohlberger, Rave, SIAM J. Sci. Comput., 2017] which allows to derive a localized a posteriori error estimator that can be employed to adaptively enrich the reduced solution space locally where needed. Numerical experiments demonstrate the potential of the proposed approach. |
| title | Local training and enrichment based on a residual localization strategy |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2404.16537 |