20 citations · 44 across the 7 of their papers we have counts for
14 papers
Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging
Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Photoacoustic imaging (PAI) can image high-resolution structures of clinical interest such as vascularity in cancerous tumor monitoring. When imaging human subjects, geometric rest…
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty qu…
Ultra-low memory seismic inversion with randomized trace estimation
Mathias Louboutin, Felix J. Herrmann
Inspired by recent work on extended image volumes that lays the ground for randomized probing of extremely large seismic wavefield matrices, we present a memory frugal and computat…
Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin +2
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneou…
Temporal blocking of finite-difference stencil operators with sparse "off-the-grid" sources
George Bisbas, Fabio Luporini, Mathias Louboutin +3
Stencil kernels dominate a range of scientific applications, including seismic and medical imaging, image processing, and neural networks. Temporal blocking is a performance optimi…
A dual formulation of wavefield reconstruction inversion for large-scale seismic inversion
Gabrio Rizzuti, Mathias Louboutin, Rongrong Wang +1
Most of the seismic inversion techniques currently proposed focus on robustness with respect to the background model choice or inaccurate physical modeling assumptions, but are not…