When does a bridge become an aeroplane?
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910718769496064 |
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| author | Dardeno, Tina A. Bull, Lawrence A. Dervilis, Nikolaos Worden, Keith |
| author_facet | Dardeno, Tina A. Bull, Lawrence A. Dervilis, Nikolaos Worden, Keith |
| contents | Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. It has been proposed that heterogeneous transfer may be accomplished via intermediate structures that bridge the gap in information between the structures of interest. A key aspect of the technique is the idea that by varying parameters such as material properties and geometry, one structure can be continuously morphed into another. The current work demonstrates the development of these interpolating structures, via case studies involving the parameterisation of (and transfer between) a simple, simulated 'bridge' and 'aeroplane'. The facetious question 'When is a bridge not an aeroplane?' has been previously asked in the context of predicting positive transfer based on structural similarity. While the obvious answer to this question is 'Always,' the current work demonstrates that in some cases positive transfer can be achieved between highly-disparate systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18406 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When does a bridge become an aeroplane? Dardeno, Tina A. Bull, Lawrence A. Dervilis, Nikolaos Worden, Keith Machine Learning Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. It has been proposed that heterogeneous transfer may be accomplished via intermediate structures that bridge the gap in information between the structures of interest. A key aspect of the technique is the idea that by varying parameters such as material properties and geometry, one structure can be continuously morphed into another. The current work demonstrates the development of these interpolating structures, via case studies involving the parameterisation of (and transfer between) a simple, simulated 'bridge' and 'aeroplane'. The facetious question 'When is a bridge not an aeroplane?' has been previously asked in the context of predicting positive transfer based on structural similarity. While the obvious answer to this question is 'Always,' the current work demonstrates that in some cases positive transfer can be achieved between highly-disparate systems. |
| title | When does a bridge become an aeroplane? |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.18406 |