Inverse Problems and Data Assimilation: A Machine Learning Approach

• URL: http://arxiv.org/abs/2410.10523v1
• Date: Mon, 14 Oct 2024 14:01:35 GMT
• Title: Inverse Problems and Data Assimilation: A Machine Learning Approach
• Authors: Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso, Andrew Stuart,
• Abstract summary: 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. As a by-product, we include a succinct mathematical treatment of various topics in machine learning.
• Score: 2.4311599697311275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 topics in machine learning.

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