59 citations · 64 across the 4 of their papers we have counts for
4 papers
DANSur_HM: Modularly incorporating higher modes in a deep learning based gravitational-wave surrogate
Osvaldo Gramaxo Freitas, Anastasios Theodoropoulos, Nino Villanueva +4
Numerical relativity (NR) simulations provide the most faithful representation of the gravitational waves (GWs) emitted by binary black hole (BBH) systems during merger. In the con…
An autoencoder-based surrogate waveform model for quasi-circular binary-black-hole mergers
Anastasios Theodoropoulos, Nino Villanueva, Osvaldo Gramaxo Freitas +6
The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate model…
A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms
Osvaldo Gramaxo Freitas, Anastasios Theodoropoulos, Nino Villanueva +6
Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without cos…
Quantum autoencoders via quantum adders with genetic algorithms
L. Lamata, U. Alvarez-Rodriguez, J. D. Martín-Guerrero +2
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum n…