Machine Learning for Inverse Problems and Data Assimilation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866916988841885696 |
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| author | Bach, Eviatar Baptista, Ricardo Sanz-Alonso, Daniel Stuart, Andrew |
| author_facet | Bach, Eviatar Baptista, Ricardo Sanz-Alonso, Daniel Stuart, Andrew |
| contents | The aim of these notes is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is primarily aimed at researchers from inverse problems and/or data assimilation who wish to see a mathematical presentation of machine learning as it pertains to their fields. As a by-product, we include a succinct mathematical treatment of various fundamental underpinning topics in machine learning, and adjacent areas of (computational) mathematics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10523 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Inverse Problems and Data Assimilation Bach, Eviatar Baptista, Ricardo Sanz-Alonso, Daniel Stuart, Andrew Machine Learning Optimization and Control The aim of these notes is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is primarily aimed at researchers from inverse problems and/or data assimilation who wish to see a mathematical presentation of machine learning as it pertains to their fields. As a by-product, we include a succinct mathematical treatment of various fundamental underpinning topics in machine learning, and adjacent areas of (computational) mathematics. |
| title | Machine Learning for Inverse Problems and Data Assimilation |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2410.10523 |