Real-time gravitational-wave science with neural posterior estimation. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 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)