Work

Machine Learning for Inverse Problems and Data Assimilation

Year: 2024

Type: preprint

Abstract: The aim of this book 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2410.10523

Authors: Bach, Eviatar, Baptista, Ricardo, Sanz-Alonso, Daniel, Stuart, Andrew

Institutions: University of Chicago, University of Toronto, California Institute of Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: California Institute of Technology, Multidisciplinary University Research Initiative, Air Force Office of Scientific Research

OpenAlex ID: W4403571037

Cites:

Cited by: 1

Related to: 10