Michael Pürrer

According to our database, Michael Pürrer authored at least 7 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., April, 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., March, 2025

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

2024

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

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

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

2021

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, Shaon Ghosh, Leïla Haegel, Alberto Iess, David Keitel, Zsuzsa Márka, Szabolcs Márka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Pürrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams

Mach. Learn. Sci. Technol., 2021