Citing Articles For: Enhancing the reliability of machine learning for gravitational wave parameter estimation with attention-based models

Machine Learning for Multi-Messenger Probes of New Physics and Cosmology: Review and Perspective

Andrea Addazi, Konstantin Belotsky, Vitaly Beylin, Timur Bikbaev, Deen Chen, Filippo Fabrocini, Stefano Giagu, Krid Jinklub, Artem Kharakhashyan, Maxim Khlopov, Vladimir Korchagin, Maxim Krasnov, Atharv Mahajan, Antonino Marcianò, Andrey Mayorov, Antonio Morais, Roman Pasechnik, Jackson Levi Said, Danila Sopin, Viktor Stasenko, and Oem Trivedi

Symmetry 18 1116 (2026)

Parameter extraction of the stochastic gravitational wave background with peak-like templates in millihertz

Heng-Sen Jiao, Hong-Bo Jin, and Yun-Long Zhang

Physics of the Dark Universe 51 102217 (2026)

CLAWDIA: a dictionary learning framework for gravitational-wave data analysis

Miquel Llorens-Monteagudo, Alejandro Torres-Forné, and José A Font

Mach. Learn.: Sci. Technol. 7 045014 (2026)

Select page:

Physical Review D

Filter

Articles Citing

Enhancing the reliability of machine learning for gravitational wave parameter estimation with attention-based models

Machine Learning for Multi-Messenger Probes of New Physics and Cosmology: Review and Perspective

Parameter extraction of the stochastic gravitational wave background with peak-like templates in millihertz

CLAWDIA: a dictionary learning framework for gravitational-wave data analysis

Filter

Filter

Article Lookup

Enter a citation