Physics-based linear regression for high-dimensional forward uncertainty quantification
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917845601878016 |
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| author | Wang, Ziqi |
| author_facet | Wang, Ziqi |
| contents | We introduce linear regression using physics-based basis functions optimized through the geometry of an inner product space. This method addresses the challenge of surrogate modeling with high-dimensional input, as the physics-based basis functions encode problem-specific information. We demonstrate the method using a proof-of-concept nonlinear random vibration example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-based linear regression for high-dimensional forward uncertainty quantification Wang, Ziqi Data Analysis, Statistics and Probability We introduce linear regression using physics-based basis functions optimized through the geometry of an inner product space. This method addresses the challenge of surrogate modeling with high-dimensional input, as the physics-based basis functions encode problem-specific information. We demonstrate the method using a proof-of-concept nonlinear random vibration example. |
| title | Physics-based linear regression for high-dimensional forward uncertainty quantification |
| topic | Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2405.08006 |