Machine Learning for Inverse Problems and Data Assimilation
Machine Learning
2025-10-07 v2 Machine Learning
Optimization and Control
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 fundamental underpinning topics in machine learning, and adjacent areas of (computational) mathematics.
Cite
@article{arxiv.2410.10523,
title = {Machine Learning for Inverse Problems and Data Assimilation},
author = {Eviatar Bach and Ricardo Baptista and Daniel Sanz-Alonso and Andrew Stuart},
journal= {arXiv preprint arXiv:2410.10523},
year = {2025}
}
Comments
305 pages