Enhancing gravitational-wave science with machine learning. - Elena Cuoco; Jade Powell; Marco Cavaglià; Kendall Ackley; Michal Bejger; Chayan Chatterjee; Michael Coughlin; Scott Coughlin; Paul Easter; Reed Essick; Hunter Gabbard; Timothy Gebhard; et al. (Mach. Learn. Sci. Technol., 2021)

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Pürrer; Jonas Wildberger; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2022)

Adapting to noise distribution shifts in flow-based gravitational-wave inference. - Jonas Wildberger; Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Pürrer; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2022)

Real-time gravitational-wave inference for binary neutron stars using machine learning. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Nihar Gupte; Michael Pürrer; Vivien Raymond; Jonas Wildberger; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2024)

pySEOBNR: A software package for the next generation of effective-one-body multipolar waveform models. - Deyan P. Mihaylov; Serguei Ossokine; Alessandra Buonanno; Hector Estelles; Lorenzo Pompili; Michael Pürrer; Antoni Ramos-Buades (SoftwareX, 2025)

Real-time inference for binary neutron star mergers using machine learning. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Nihar Gupte; Michael Pürrer; Vivien Raymond; Jonas Wildberger; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (Nat., 2025)

GWSurrogate: A Python package for gravitational wave surrogate models. - Scott E. Field; Vijay Varma; Jonathan Blackman; Bhooshan Gadre; Chad R. Galley; Tousif Islam; Keefe Mitman; Michael Pürrer; Adhrit Ravichandran; Mark A. Scheel; Leo C. Stein; Jooheon Yoo (J. Open Source Softw., 2025)