Comparing RSVD and Krylov methods for linear inverse problems. - Nick Luiken; Tristan van Leeuwen (Comput. Geosci., 2020)

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

Relaxed Regularization for Linear Inverse Problems. - Nick Luiken; Tristan van Leeuwen (SIAM J. Sci. Comput., 2021)

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

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)

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

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