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Call-for-papers: “Machine Learning in Gravitational-Wave Science”

New article collection on applications of machine learning techniques in gravitational-wave research

Published in Physics, Computational Sciences, and Statistics

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Frank Schulz
Publishing Editor Physics & Astronomy, Springer Nature
Call-for-papers: “Machine Learning in Gravitational-Wave Science”
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Machine Learning in Gravitational-Wave Science

A collection of articles on applications of machine learning techniques in gravitational-wave research. While the Living Review "Applications of machine…

The journals General Relativity and Gravitation and Living Reviews in Relativity have opened a joint Topical Collection on “Machine Learning in Gravitational-Wave Science”, which aims to include review articles and original research.

While the recent Living Review “Applications of machine learning in gravitational-wave research with current interferometric detectors” by Cuoco et al. (2025) serves as a general introduction to the topic, we encourage authors to submit their work on specific applications of machine learning methods, neural networks, and deep learning techniques in GW data analysis, including noise reduction and mitigation, signal detection, parameter estimation, classification and interpretation of astrophysical sources to the journal General Relativity and Gravitation.

Go to the profile of Frank Schulz
Frank Schulz
Publishing Editor Physics & Astronomy, Springer Nature

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Gravitational Physics
Physical Sciences > Physics and Astronomy > Gravitational Physics
Classical and Quantum Gravity
Physical Sciences > Physics and Astronomy > Theoretical, Mathematical and Computational Physics > Classical and Quantum Gravity
Machine Learning
Mathematics and Computing > Computer Science > Artificial Intelligence > Machine Learning
Data Analysis and Big Data
Mathematics and Computing > Statistics > Data Analysis and Big Data

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