PyLops - A Linear-Operator Python Library for large scale optimization. - Matteo Ravasi; Ivan Vasconcelos (CoRR, 2019)

On the implementation of large-scale integral operators with modern HPC solutions - Application to 3D Marchenko imaging by least-squares inversion. - Matteo Ravasi; Ivan Vasconcelos (CoRR, 2020)

PyLops - A linear-operator Python library for scalable algebra and optimization. - Matteo Ravasi; Ivan Vasconcelos (SoftwareX, 2020)

The potential of self-supervised networks for random noise suppression in seismic data. - Claire Birnie; Matteo Ravasi; Tariq Alkhalifah; Sixiu Liu (CoRR, 2021)

Time-Domain Multidimensional Deconvolution: A Physically Reliable and Stable Preconditioned Implementation. - David Vargas 003; Ivan Vasconcelos; Matteo Ravasi; Nick Luiken (Remote. Sens., 2021)

Accelerating Seismic Redatuming Using Tile Low-Rank Approximations on NEC SX-Aurora TSUBASA. - Yuxi Hong; Hatem Ltaief; Matteo Ravasi; Laurent Gatineau; David E. Keyes (Supercomput. Front. Innov., 2021)

A hybrid approach to seismic deblending: when physics meets self-supervision. - Nick Luiken; Matteo Ravasi; Claire E. Birnie (CoRR, 2022)

Deep Preconditioners and their application to seismic wavefield processing. - Matteo Ravasi (CoRR, 2022)

Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion. - Muhammad Izzatullah; Tariq Alkhalifah; Juan Romero; Miguel Corrales; Nick Luiken; Matteo Ravasi (CoRR, 2022)

Responsibly Reckless Matrix Algorithms for HPC Scientific Applications. - Hatem Ltaief; Marc G. Genton; Damien Gratadour; David E. Keyes; Matteo Ravasi (Comput. Sci. Eng., 2022)

Intelligent Seismic Deblending Through Deep Preconditioner. - Weiwei Xu; Vincenzo Lipari; Paolo Bestagini; Matteo Ravasi; Wenchao Chen; Stefano Tubaro (IEEE Geosci. Remote. Sens. Lett., 2022)

Stochastic Multi-Dimensional Deconvolution. - Matteo Ravasi; Tamin Selvan; Nick Luiken (IEEE Trans. Geosci. Remote. Sens., 2022)

A deep learning-based approach to increase efficiency in the acquisition of ultrasonic non-destructive testing datasets. - Nick Luiken; Matteo Ravasi (CVPR Workshops, 2023)

Scaling the "Memory Wall" for Multi-Dimensional Seismic Processing with Algebraic Compression on Cerebras CS-2 Systems. - Hatem Ltaief; Yuxi Hong; Leighton Wilson; Mathias Jacquelin; Matteo Ravasi; David Elliot Keyes (SC, 2023)

Steering Customized AI Architectures for HPC Scientific Applications. - Hatem Ltaief; Yuxi Hong; Adel Dabah; Rabab Alomairy; Sameh Abdulah; Chris Goreczny; Pawel Gepner; Matteo Ravasi; Damien Gratadour; David E. Keyes (ISC, 2023)

PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks. - Francesco Brandolin; Matteo Ravasi; Tariq Alkhalifah (CoRR, 2023)

Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising. - Claire Birnie; Matteo Ravasi (CoRR, 2023)

IntraSeismic: a coordinate-based learning approach to seismic inversion. - Juan Romero; Wolfgang Heidrich; Nick Luiken; Matteo Ravasi (CoRR, 2023)

Multidimensional deconvolution with shared bases. - Daria Sushnikova; Matteo Ravasi; David E. Keyes (CoRR, 2024)

High performance computing seismic redatuming by inversion with algebraic compression and multiple precisions. - Yuxi Hong; Hatem Ltaief; Matteo Ravasi; David E. Keyes (Int. J. High Perform. Comput. Appl., 2024)

PyProximal - scalable convex optimization in Python. - Matteo Ravasi; Marcus Valtonen Örnhag; Nick Luiken; Olivier Leblanc; Eneko Uruñuela (J. Open Source Softw., 2024)

Geophysical inverse problems with measurement-guided diffusion models. - Matteo Ravasi (CoRR, 2025)

PyLops-MPI - MPI Powered PyLops with mpi4py. - Rohan Babbar; Matteo Ravasi; Yuxi Hong (J. Open Source Softw., 2025)