Removing Inefficiencies from Scientific Code: The Study of the Higgs Boson Couplings to Top Quarks. - André Pereira 002; António Onofre; Alberto Proença (ICCSA (4), 2014)

Tuning pipelined scientific data analyses for efficient multicore execution. - André Pereira 002; António Onofre; Alberto Proença (HPCS, 2016)

Gravitational-wave parameter inference using Deep Learning. - João D. Álvares; José A. Font; Felipe F. Freitas; Osvaldo Gramaxo Freitas; António P. Morais; Solange Nunes; António Onofre; Alejandro Torres-Forné (CBMI, 2021)

Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection. - João Aveiro; Felipe F. Freitas; Márcio Ferreira; António Onofre; Constança Providência; Gonçalo Gonçalves; José A. Font (CoRR, 2022)

Machine-Learning Love: classifying the equation of state of neutron stars with Transformers. - Gonçalo Gonçalves; Márcio Ferreira; João Aveiro; António Onofre; Felipe F. Freitas; Constança Providência; José A. Font (CoRR, 2022)

Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams. - Tiago S. Fernandes; Samuel J. Vieira; António Onofre; Juan Calderón Bustillo; Alejandro Torres-Forné; José A. Font (CoRR, 2023)

NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms. - Osvaldo Gramaxo Freitas; Anastasios Theodoropoulos; Nino Villanueva; Tiago S. Fernandes; Solange Nunes; José A. Font; António Onofre; Alejandro Torres-Forné; José D. Martín-Guerrero (CoRR, 2024)

Comparison of neural network architectures for feature extraction from binary black hole merger waveforms. - Osvaldo Gramaxo Freitas; Juan Calderón Bustillo; José A. Font; Solange Nunes; António Onofre; Alejandro Torres-Forné (Mach. Learn. Sci. Technol., 2024)